{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85f88996-cf08-4a53-b102-247e38229134",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:52:55.167535Z",
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     "iopub.status.idle": "2024-11-10T13:52:57.963296Z",
     "msg_id": "648d43d8-8602-4ac5-973e-b81fffcfca6f",
     "shell.execute_reply": "2024-11-10T13:52:57.962545Z",
     "shell.execute_reply.started": "2024-11-10T13:52:55.167505Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import os \n",
    "import gc\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "import lightgbm as lgb \n",
    "from lightgbm import log_evaluation, early_stopping\n",
    "import xgboost as xgb \n",
    "import copy \n",
    "\n",
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib import rcParams\n",
    "rcParams[\"font.family\"] = \"SimHei\"\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from itertools import combinations \n",
    "import pickle\n",
    "# from bayes_opt import BayesianOptimization\n",
    "# import optuna\n",
    "from functools import partial\n",
    "\n",
    "import networkx as nx \n",
    "from itertools import combinations\n",
    "from functools import partial\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,roc_auc_score,log_loss\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import sklearn.ensemble as ensemble\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import svm\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# import lightgbm as lgb\n",
    "# from lightgbm import LGBMClassifier\n",
    "# from lightgbm import log_evaluation, early_stopping\n",
    "\n",
    "import catboost as cbt\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "from scipy import stats,integrate\n",
    "from scipy.stats import ks_2samp\n",
    "#from scipy.stats import kssamp\n",
    "from scipy.stats import pearsonr\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "from scipy.stats import kstest\n",
    "\n",
    "import toad\n",
    "\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f923bd-1bec-46b2-8824-62214d871543",
   "metadata": {},
   "source": [
    "# 工具函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec48db7-13b0-42ab-bc57-4b98a0db7741",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3b8f266-593c-413e-8267-9960e7d5eb96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:02.968172Z",
     "iopub.status.busy": "2024-11-10T13:53:02.967067Z",
     "iopub.status.idle": "2024-11-10T13:53:02.978998Z",
     "msg_id": "1bdef27f-87f3-448b-96d4-481e04174061",
     "shell.execute_reply": "2024-11-10T13:53:02.978201Z",
     "shell.execute_reply.started": "2024-11-10T13:53:02.968134Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_data(file_name, num_rows=None):\n",
    "    train_path = \"/home/mole/work/contest/train\"\n",
    "    test_path = \"/home/mole/work/contest/A\"\n",
    "    df_train = pd.read_csv(os.path.join(train_path, file_name + \"_T.csv\"), nrows=num_rows)\n",
    "    df_test = pd.read_csv(os.path.join(test_path, file_name + \"_A.csv\"), nrows=num_rows)\n",
    "    df_train[\"is_train\"] = 1\n",
    "    df_test[\"is_train\"] = 0\n",
    "    \n",
    "    df = pd.concat(objs=[df_train, df_test],axis=0)\n",
    "    df.rename(mapper = {'DATA_DAT': '数据日期', 'CUST_NO': '客户编号', 'OPTO': '经营期限至', 'OPFROM': '经营期限自', 'ENTSTATUS': '经营状态', 'REGCAP': '注册资本', 'ESDATE': '成立日期', 'FRNAME': '法定代表人/负责人/执行事务合伙人', 'ENTTYPE_CD': '企业（机构）类型编码', 'REGPROVIN_CD': '所在省份编码', 'INDS_CD': '国民经济行业代码', 'ALTDATE': '变更日期', 'ALTITEM': '变更事项', 'PERNAME': '人员姓名', 'POSITIONCODE': '职位代码', 'PERSONAMOUNT': '人员总数量', 'WEBTYPE': '网站（网店）类型', 'WEBSITNAME': '网站（网店）名称', 'DOMAIN': '网站（网店）地址', 'ANCHEDATE': '年报日期', 'ANCHEYEAR': '年报年份', 'EXECMONEY': '执行标的', 'REGDATECLEAN': '立案时间', 'COURTNAME': '执行法院', 'CASECODE': '案号', 'PUBLISHDATECLEAN': '发布时间', 'GISTID': '执行依据文号', 'PERFORMANCE': '被执行人履行情况', 'REGDATE': '立案时间', 'FINALDATE': '终本日期', 'UNPERFMONEY': '未履行金额', 'CONDATE': '出资日期', 'SUBCONAM': '认缴出资额（万元）', 'FUNDEDRATIO': '出资比例', 'INVTYPE': '股东类型', 'CONFORM': '出资方式', 'SH_CUST_NO': '股东客户编号', 'BTD_BEGINDATE': '所属日期起', 'BTD_ENDDATE': '所属日期止', 'BTD_COLLECTCODE': '征收项目代码', 'BTD_DECLARDATE': '申报日期', 'BTD_DECLARTERM': '申报期限', 'BTD_TOTALSALE': '全部销售收入', 'BTD_TAXABLESALE': '应税销售收入', 'BTD_TAXPAYABLE': '应纳税额', 'BTD_DEDUCTAMOUNT': '减免税额', 'TR_DAT': '交易日期', 'TR_CD': '交易代码', 'CHANL_CD': '渠道代码', 'ABS_INFO': '摘要信息', 'CPT_TYP_CD': '交易对手类型代码', 'ARG_ACCT_BAL': '合约账户余额', 'ACTG_DIRET_CD': '记账方向代码', 'TRS_CSH_IND': '现转标识', 'CSH_EX_IND': '钞汇标识', 'RMB_TR_AMT': '折人民币交易金额', 'CPT_INTL_FE_CUST_IND': '对手方行内客户标识', 'INT_BNK_TR_IND': '是否跨行交易', 'SAME_NAM_IND': '同名账户标识', 'CPT_CUST_NO': '交易对手客户编号'},\n",
    "              axis=1,\n",
    "              inplace=True\n",
    "             )\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b92cbe60-099a-4da6-b9cd-4e6cf7e7fb2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:25:53.159602Z",
     "iopub.status.busy": "2024-11-10T13:25:53.159222Z",
     "iopub.status.idle": "2024-11-10T13:25:53.165829Z",
     "msg_id": "fc534a79-5aa7-4be0-ac27-40f886ff6c37",
     "shell.execute_reply": "2024-11-10T13:25:53.165130Z",
     "shell.execute_reply.started": "2024-11-10T13:25:53.159573Z"
    }
   },
   "outputs": [],
   "source": [
    "def agg_statistics(df, group_cols, agg_functions, name_flag, p=False):\n",
    "    \"\"\"\n",
    "    分组聚合。\n",
    "    \"\"\"\n",
    "    ga = df.groupby(by=group_cols).agg(agg_functions)\n",
    "    ga.columns = [\"{}_{}_{}\".format(e[0], e[1], name_flag) for e in ga.columns.tolist()]\n",
    "    ga.reset_index(inplace=True)\n",
    "    \n",
    "    new_cols = [col for col in ga.columns.tolist() if col not in group_cols]\n",
    "    if p is True:\n",
    "        print(\"新聚合特征：\\n\", new_cols)\n",
    "    \n",
    "    return ga, new_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d970bd24-4399-446d-9b97-fd9b5f9db18d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:05.195281Z",
     "iopub.status.busy": "2024-11-10T13:53:05.194786Z",
     "iopub.status.idle": "2024-11-10T13:53:05.204499Z",
     "msg_id": "7b21c135-f7b9-43ab-88fd-69db546ce5ac",
     "shell.execute_reply": "2024-11-10T13:53:05.203666Z",
     "shell.execute_reply.started": "2024-11-10T13:53:05.195248Z"
    }
   },
   "outputs": [],
   "source": [
    "# 趋势差分特征衍生\n",
    "def get_kurt(series_x):\n",
    "    kurt = series_x.kurt()\n",
    "    return kurt\n",
    "    \n",
    "def trend_indicator(df, group_dim_1, group_dim_2, agg_functions, name_flag, offset=-1):\n",
    "    \"\"\"\n",
    "    group_dim_1：第一维度\n",
    "    group_dim_2：第二维度\n",
    "    \"\"\"\n",
    "    df = df.sort_values(by=group_dim_2, ascending=True)\n",
    "    ga = df.groupby(by=[group_dim_1, group_dim_2]).agg(agg_functions)\n",
    "    ga.columns = [\"{}_{}_{}\".format(e[0], name_flag, e[1]) for e in ga.columns.tolist()]\n",
    "    new_features = ga.columns.tolist()\n",
    "    ga.reset_index(inplace=True)\n",
    "\n",
    "    diff_new_features = []\n",
    "    for fea in new_features:\n",
    "        t = \"一阶差分_{}_{}\".format(fea, offset)\n",
    "        diff_new_features.append(t)\n",
    "        ga[t] = ga.groupby(by=group_dim_1)[fea].diff(offset) # -1\n",
    "\n",
    "    all_new_features = new_features + diff_new_features\n",
    "    agg_functions_tmp = {}\n",
    "    for fea in all_new_features:\n",
    "        agg_functions_tmp.update({fea:['last','mean','skew',get_kurt,'std','sum','max','min']})    \n",
    "\n",
    "    ga_new, _ = agg_statistics(df=ga, group_cols=[group_dim_1], agg_functions=agg_functions_tmp, name_flag=\"差分特征_{}\".format(name_flag))\n",
    "\n",
    "    return ga_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb2e163e-f4fc-45f3-a2c4-0d2aede47a94",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a98d14f-2ba9-4fdb-8843-a285ea282c94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:06.910712Z",
     "iopub.status.busy": "2024-11-10T13:53:06.910018Z",
     "iopub.status.idle": "2024-11-10T13:53:06.925279Z",
     "msg_id": "4187c9d5-2717-41af-b2d0-335437b5b758",
     "shell.execute_reply": "2024-11-10T13:53:06.924477Z",
     "shell.execute_reply.started": "2024-11-10T13:53:06.910681Z"
    }
   },
   "outputs": [],
   "source": [
    "def LGB_model(\n",
    "              X=None,\n",
    "              y=None,\n",
    "              params=None,\n",
    "              num_boost_round=10000,\n",
    "              categorical_feature=None,\n",
    "              cv=StratifiedKFold(n_splits=5, shuffle=True, random_state=2022)\n",
    "             ):\n",
    "\n",
    "    callbacks = [log_evaluation(period=100), early_stopping(stopping_rounds = 200)]\n",
    "    if params is None:\n",
    "        params = {\n",
    "                    \"boost\":\"gbdt\",\n",
    "                    \"objective\":\"binary\",\n",
    "                    \"metric\":\"auc\",\n",
    "                    \"max_depth\":6,\n",
    "                    \"learning_rate\":0.05,\n",
    "                    \"feature_fraction\":0.85,\n",
    "                    \"bagging_fraction\":0.85,\n",
    "                    \"bagging_freq\":5,\n",
    "                    \"max_bin\":56,\n",
    "                    \"seed\":2022,    # 随机数种子，必须设置\n",
    "                    \"verbose\":-1\n",
    "                }\n",
    "\n",
    "    columns = X.columns.tolist() \n",
    "\n",
    "    y_oof = np.zeros(X.shape[0])\n",
    "    score = 0\n",
    "    score_auc = 0\n",
    "    clfs = []\n",
    "    ks_list = []\n",
    "    for k, (trian_index, valid_index) in enumerate(cv.split(X, y)):\n",
    "\n",
    "        X_train, y_train = X.values[trian_index], y.values[trian_index]\n",
    "        X_valid, y_valid = X.values[valid_index], y.values[valid_index]\n",
    "        train_D = lgb.Dataset(data=X_train, label=y_train, feature_name=columns, categorical_feature=categorical_feature)\n",
    "        valid_D = lgb.Dataset(data=X_valid, label=y_valid, feature_name=columns, categorical_feature=categorical_feature, reference=train_D)\n",
    "\n",
    "        clf = lgb.train(params=params,\n",
    "                        train_set=train_D,\n",
    "                        valid_sets=[train_D, valid_D],\n",
    "                        valid_names=[\"Train\", \"Valid\"],\n",
    "                        num_boost_round=num_boost_round,\n",
    "                        callbacks = callbacks\n",
    "                        )\n",
    "        y_pred_valid = clf.predict(X_valid, num_iteration=clf.best_iteration)\n",
    "        y_oof[valid_index] = y_pred_valid\n",
    "        print(\"=======================================\")\n",
    "        print(\"第 {} 折，当前 KS = {:.6}\".format(k+1, get_KS(y_valid, y_pred_valid)))\n",
    "        print(\"=======================================\")\n",
    "        score = score + get_KS(y_valid, y_pred_valid)\n",
    "        score_auc = score_auc + roc_auc_score(y_valid, y_pred_valid)\n",
    "        ks_list.append(get_KS(y_valid, y_pred_valid))\n",
    "\n",
    "        # 测算最佳阈值\n",
    "\n",
    "        del X_train, X_valid, y_train, y_valid\n",
    "        gc.collect()\n",
    "\n",
    "        clfs.append(clf)\n",
    "\n",
    "    ks_list.append(score/(k+1))\n",
    "    ks_list.append(get_KS(y, y_oof))\n",
    "    print(\"平均 KS = {:.6}\".format(score/(k+1)))\n",
    "    print(\"Out of folds KS = {:.6}\".format(get_KS(y, y_oof)))\n",
    "\n",
    "    print(\"平均 AUC = {:.6}\".format(score_auc/(k+1)))\n",
    "    auc = roc_auc_score(y, y_oof)\n",
    "    print(\"Out of folds AUC = {:.6}\".format(auc))\n",
    "\n",
    "    return clfs, ks_list, get_KS(y, y_oof)\n",
    "\n",
    "## 计算KS\n",
    "def get_KS(y_true, y_pred):\n",
    "    fpr, tpr, _ = roc_curve(y_true, y_pred)\n",
    "    return max(abs((fpr-tpr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003e0989-5f64-42b8-a60b-a322e6aa8035",
   "metadata": {},
   "source": [
    "## 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d93c8ff-cddb-4e50-8764-814ddb6c88db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:09.955805Z",
     "iopub.status.busy": "2024-11-10T13:53:09.955327Z",
     "iopub.status.idle": "2024-11-10T13:53:09.962941Z",
     "msg_id": "cb30e40d-896b-4c5e-bedd-619fc8610d8a",
     "shell.execute_reply": "2024-11-10T13:53:09.962183Z",
     "shell.execute_reply.started": "2024-11-10T13:53:09.955773Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_feature_imp(clfs, imp_type='gain', feature_names=None, top_n=25):\n",
    "    \"\"\"\n",
    "    获取模型训练时的特征重要性，并绘图。\n",
    "    \"\"\"\n",
    "    feature_importances = pd.DataFrame()\n",
    "    feature_importances['feature'] = feature_names\n",
    "    for i, clf in enumerate(clfs):\n",
    "        feature_importances[str(i)] = clf.feature_importance(imp_type)\n",
    "    feature_importances['average'] = np.exp(np.log1p(feature_importances[[str(i) for i in range(len(clfs))]]).mean(axis=1))\n",
    "    \n",
    "    plt.figure(figsize=(20, 16))\n",
    "    sns.barplot(data=feature_importances.sort_values(by='average', ascending=False).head(top_n), x='average', y='feature');\n",
    "    plt.title('{} TOP feature importance over {} folds average gain'.format(top_n, 5));\n",
    "    return feature_importances.sort_values(by='average', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b765a82-eb46-424c-9c43-bec755069d11",
   "metadata": {},
   "source": [
    "# 标签信息表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3adb0a3-62d0-4672-9adc-fb30c177408b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:11.384979Z",
     "iopub.status.busy": "2024-11-10T13:53:11.384528Z",
     "iopub.status.idle": "2024-11-10T13:53:11.462518Z",
     "msg_id": "88e74635-69d5-4eb8-a2ba-9a4947fd5b30",
     "shell.execute_reply": "2024-11-10T13:53:11.461805Z",
     "shell.execute_reply.started": "2024-11-10T13:53:11.384950Z"
    }
   },
   "outputs": [],
   "source": [
    "TARGET = get_data(\"XW_ENTINFO_TARGET\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706639ed-196a-4608-bca9-b1053c5dc23b",
   "metadata": {},
   "source": [
    "# 企业基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb7a1342-fe8c-45d2-9938-f4a97bbeb8c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:16.028533Z",
     "iopub.status.busy": "2024-11-10T13:53:16.028058Z",
     "iopub.status.idle": "2024-11-10T13:53:16.399728Z",
     "msg_id": "6993af52-91bf-4dce-9cfe-15bc55a20a15",
     "shell.execute_reply": "2024-11-10T13:53:16.398959Z",
     "shell.execute_reply.started": "2024-11-10T13:53:16.028501Z"
    }
   },
   "outputs": [],
   "source": [
    "BASIC = get_data(\"XW_ENTINFO_BASIC\").merge(TARGET[[\"客户编号\",\"FLAG\"]], how=\"left\", on=\"客户编号\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b7f9c3-3e66-41f2-a206-9a693328dc6f",
   "metadata": {},
   "source": [
    "# 企业金融性交易明细"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930f5641-8b27-4437-be2e-cfb14688baed",
   "metadata": {},
   "source": [
    "## 第二版简单逻辑特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a256bc57-a039-4233-ac77-1dc73cd79a03",
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    "execution": {
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     "shell.execute_reply.started": "2024-11-10T13:53:26.985479Z"
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   "outputs": [],
   "source": [
    "def 企业金融性交易明细_第二版简单逻辑特征():\n",
    "    data = get_data(\"XW_ENTINFO_FNCL_TR_DTAL\").merge(TARGET[[\"客户编号\",\"FLAG\"]], how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    # 匹配企业成立日期\n",
    "    data = data.merge(BASIC[[\"客户编号\",\"成立日期\",\"数据日期\"]], how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    # 最近一笔交易日期 与 采样日期有差距，范围是 0~88\n",
    "    data.rename(mapper={\"数据日期\":\"采样日期\"}, inplace=True,axis=1)\n",
    "    \n",
    "    data[\"交易日期\"] = data[\"交易日期\"].astype(\"str\").astype(\"datetime64[ns]\")\n",
    "    data[\"采样日期\"] = data[\"采样日期\"].astype(\"str\").astype(\"datetime64[ns]\")\n",
    "    data[\"采样日期_距离_交易日期_天数\"] = (data[\"采样日期\"] - data[\"交易日期\"]).dt.days # 0-90\n",
    "    \n",
    "    data[\"采样日期_距离_交易日期_30天\"] = data[\"采样日期_距离_交易日期_天数\"] // 30 + 1\n",
    "    \n",
    "    # 衍生客户采样时最近一周是否有交易\n",
    "    data[\"交易日期_距离采样日期_是否7天内\"] = data[\"采样日期_距离_交易日期_天数\"].apply(lambda x: 1 if x<7 else 0)\n",
    "    data[\"交易日期_距离采样日期_是否30天内\"] = data[\"采样日期_距离_交易日期_天数\"].apply(lambda x: 1 if x<30 else 0)\n",
    "    data[\"交易日期_距离采样日期_是否120天内\"] = data[\"采样日期_距离_交易日期_天数\"].apply(lambda x: 1 if x<=120 else 0)\n",
    "    \n",
    "    # 剔除同名转账\n",
    "    fea_同名转账占比 = data[[\"客户编号\",\"同名账户标识\"]].groupby(by=\"客户编号\").agg(\"mean\")\n",
    "    fea_同名转账占比.columns = [\"采样前120天_同名转账笔数占比\"]\n",
    "    fea_同名转账占比.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    # 剔除同名转账\n",
    "    data = data[data[\"同名账户标识\"] == 0]\n",
    "    \n",
    "    # PART1-采样前一周内金融性交易笔数\n",
    "    fea_采样前一周交易笔数 = data[[\"客户编号\",\"交易日期_距离采样日期_是否7天内\",\"交易日期_距离采样日期_是否30天内\",\"交易日期_距离采样日期_是否120天内\"]].groupby(by=\"客户编号\").agg(\"sum\")\n",
    "    fea_采样前一周交易笔数.columns = [\"采样前7天内金融性交易笔数\",\"采样前30天内金融性交易笔数\",\"采样前120天内金融性交易笔数\"]\n",
    "    fea_采样前一周交易笔数[\"采样前7天内金融性交易笔数_占比\"] = fea_采样前一周交易笔数[\"采样前7天内金融性交易笔数\"] / fea_采样前一周交易笔数[\"采样前120天内金融性交易笔数\"]\n",
    "    fea_采样前一周交易笔数[\"采样前30天内金融性交易笔数_占比\"] = fea_采样前一周交易笔数[\"采样前30天内金融性交易笔数\"] / fea_采样前一周交易笔数[\"采样前120天内金融性交易笔数\"]\n",
    "    fea_采样前一周交易笔数.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    # 是否为不常见类型的交易，频率低于 5‰，约250类\n",
    "    tail_ind = data[\"交易代码\"].value_counts(normalize=True)[data[\"交易代码\"].value_counts(normalize=True) < 0.01].index.tolist()\n",
    "    data[\"是否为少见交易类型\"] = data[\"交易代码\"].apply(lambda x: 1 if x in tail_ind else 0)\n",
    "    \n",
    "    fea_不常见交易 = data[[\"客户编号\",\"是否为少见交易类型\"]].groupby(by=\"客户编号\").agg([\"sum\",\"count\"])\n",
    "    fea_不常见交易.columns = [\"采样前120天少见类型交易笔数\",\"总交易笔数\"]\n",
    "    fea_不常见交易[\"采样前120天内少见类型交易笔数_占比\"] = fea_不常见交易[\"采样前120天少见类型交易笔数\"] / fea_不常见交易[\"总交易笔数\"]\n",
    "    fea_不常见交易.reset_index(drop=False, inplace=True)\n",
    "    fea_不常见交易.drop(columns=[\"总交易笔数\"],axis=1,inplace=True)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # 是否为不常见渠道的交易，频率低于 1％，29/40类， 坏率0.024 VS 0.007\n",
    "    tail_ind = data[\"渠道代码\"].value_counts(normalize=True)[data[\"渠道代码\"].value_counts(normalize=True) < 0.01].index.tolist()\n",
    "    data[\"是否为少见渠道交易\"] = data[\"渠道代码\"].apply(lambda x: 1 if x in tail_ind else 0)\n",
    "    # 是否为最常见两类渠道的交易，坏率 0.015 VS 0.027\n",
    "    top2_ind = [\"9b211b103ee15fecafea4a16f3ec62f1\", \"2be0e6cf9dab3119ff0391c27dec73c2\"]\n",
    "    data[\"是否为频率最高的两类渠道交易\"] = data[\"渠道代码\"].apply(lambda x: 1 if x in top2_ind else 0)\n",
    "    \n",
    "    fea_分渠道交易 = data[[\"客户编号\",\"是否为少见渠道交易\",\"是否为频率最高的两类渠道交易\"]].groupby(by=\"客户编号\").agg({\"是否为少见渠道交易\":[\"sum\",\"count\"],\"是否为频率最高的两类渠道交易\":[\"sum\"]})\n",
    "    fea_分渠道交易.columns = [\"采样前120天少见渠道交易笔数\",\"总交易笔数\",\"采样前120天频率最高两类渠道交易笔数\"]\n",
    "    fea_分渠道交易[\"采样前120天内少见渠道交易笔数_占比\"] = fea_分渠道交易[\"采样前120天少见渠道交易笔数\"] / fea_分渠道交易[\"总交易笔数\"]\n",
    "    fea_分渠道交易[\"采样前120天内频率最高两类渠道交易笔数_占比\"] = fea_分渠道交易[\"采样前120天频率最高两类渠道交易笔数\"] / fea_分渠道交易[\"总交易笔数\"]\n",
    "    fea_分渠道交易.reset_index(drop=False, inplace=True)\n",
    "    fea_分渠道交易.drop(columns=[\"总交易笔数\"],axis=1,inplace=True)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # 交易对手类型，只有两类取值，交易数量及交易坏率，无明显差异\n",
    "    # 统计客户对公客户、个人客户交易笔数/金额\n",
    "    data[\"交易对手类型代码\"] = data[\"交易对手类型代码\"].map({\"89e3310a438292017fbbb0f2f799f948\":\"对公\", \"95d423a88e97fd7197c280e86489cef5\":\"个人\"})\n",
    "    fea_对公个人交易金额 = data[[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    fea_对公个人交易金额.columns = [\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_对公个人交易金额.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    t_对公 = data[data[\"交易对手类型代码\"] == \"对公\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_对公.columns = [\"采样前120天客户_对公_折人民币交易总金额\"]\n",
    "    t_对公.reset_index(drop=False, inplace=True)\n",
    "    t_个人 = data[data[\"交易对手类型代码\"] == \"个人\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_个人.columns = [\"采样前120天客户_个人_折人民币交易总金额\"]\n",
    "    t_个人.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    fea_对公个人交易金额 = fea_对公个人交易金额.merge(t_对公, how=\"left\", on=\"客户编号\")\n",
    "    fea_对公个人交易金额 = fea_对公个人交易金额.merge(t_个人, how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    fea_对公个人交易金额 = fea_对公个人交易金额.fillna(0)\n",
    "    fea_对公个人交易金额[\"对公交易金额占比\"] = fea_对公个人交易金额[\"采样前120天客户_对公_折人民币交易总金额\"] / fea_对公个人交易金额[\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_对公个人交易金额[\"对公_个人交易金额占比\"] = fea_对公个人交易金额[\"采样前120天客户_对公_折人民币交易总金额\"] / fea_对公个人交易金额[\"采样前120天客户_个人_折人民币交易总金额\"]\n",
    "    fea_对公个人交易金额 = fea_对公个人交易金额.drop(columns=[\"采样前120天客户折人民币交易总金额\"])\n",
    "    \n",
    "    # 记账方向代码，只有两类取值\n",
    "    data[\"记账方向代码\"] = data[\"记账方向代码\"].map({\"16459755d990723240edb88e34a13fab\":\"转出\", \"1250d7cb654a81c7b9366dabf57fe62b\":\"转入\"})\n",
    "    fea_转入转出交易金额 = data[[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    fea_转入转出交易金额.columns = [\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_转入转出交易金额.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    t_转出 = data[data[\"记账方向代码\"] == \"转出\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_转出.columns = [\"采样前120天客户_转出_折人民币交易总金额\"]\n",
    "    t_转出.reset_index(drop=False, inplace=True)\n",
    "    t_转入 = data[data[\"记账方向代码\"] == \"转入\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_转入.columns = [\"采样前120天客户_转入_折人民币交易总金额\"]\n",
    "    t_转入.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    fea_转入转出交易金额 = fea_转入转出交易金额.merge(t_转出, how=\"left\", on=\"客户编号\")\n",
    "    fea_转入转出交易金额 = fea_转入转出交易金额.merge(t_转入, how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    fea_转入转出交易金额 = fea_转入转出交易金额.fillna(0)\n",
    "    fea_转入转出交易金额[\"转出金额占比\"] =  fea_转入转出交易金额[\"采样前120天客户_转出_折人民币交易总金额\"] / fea_转入转出交易金额[\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_转入转出交易金额[\"转出_转入金额占比\"] =  fea_转入转出交易金额[\"采样前120天客户_转出_折人民币交易总金额\"] / fea_转入转出交易金额[\"采样前120天客户_转入_折人民币交易总金额\"]\n",
    "    fea_转入转出交易金额 = fea_转入转出交易金额.drop(columns=[\"采样前120天客户折人民币交易总金额\"])\n",
    "    \n",
    "    # 是否跨行交易，只有两类取值\n",
    "    data[\"是否跨行交易\"] = data[\"是否跨行交易\"].map({0.0:\"行内\", 1.0:\"跨行\"})\n",
    "    fea_是否跨行交易金额 = data[[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    fea_是否跨行交易金额.columns = [\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_是否跨行交易金额.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    t_行内 = data[data[\"是否跨行交易\"] == \"行内\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_行内.columns = [\"采样前120天客户_行内_折人民币交易总金额\"]\n",
    "    t_行内.reset_index(drop=False, inplace=True)\n",
    "    t_跨行 = data[data[\"是否跨行交易\"] == \"跨行\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_跨行.columns = [\"采样前120天客户_跨行_折人民币交易总金额\"]\n",
    "    t_跨行.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    fea_是否跨行交易金额 = fea_是否跨行交易金额.merge(t_行内, how=\"left\", on=\"客户编号\")\n",
    "    fea_是否跨行交易金额 = fea_是否跨行交易金额.merge(t_跨行, how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    fea_是否跨行交易金额 = fea_是否跨行交易金额.fillna(0)\n",
    "    fea_是否跨行交易金额[\"行内转账金额占比\"] = fea_是否跨行交易金额[\"采样前120天客户_行内_折人民币交易总金额\"] / fea_是否跨行交易金额[\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_是否跨行交易金额[\"行内_行外转账金额占比\"] = fea_是否跨行交易金额[\"采样前120天客户_行内_折人民币交易总金额\"] / fea_是否跨行交易金额[\"采样前120天客户_跨行_折人民币交易总金额\"]\n",
    "    fea_是否跨行交易金额 = fea_是否跨行交易金额.drop(columns=[\"采样前120天客户折人民币交易总金额\"])\n",
    "    \n",
    "    # 行内客户转账\n",
    "    data[\"对手方行内客户标识\"] = data[\"对手方行内客户标识\"].map({0.0:\"行内\", 1.0:\"行外\"})\n",
    "    fea_是否行内对手 = data[[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    fea_是否行内对手.columns = [\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_是否行内对手.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    t_行内 = data[data[\"对手方行内客户标识\"] == \"行内A\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_行内.columns = [\"采样前120天客户_行内对手_折人民币交易总金额\"]\n",
    "    t_行内.reset_index(drop=False, inplace=True)\n",
    "    t_行外 = data[data[\"对手方行内客户标识\"] == \"跨行A\"][[\"客户编号\",\"折人民币交易金额\"]].groupby(by=\"客户编号\").sum()\n",
    "    t_行外.columns = [\"采样前120天客户_行外对手_折人民币交易总金额\"]\n",
    "    t_行外.reset_index(drop=False, inplace=True)\n",
    "    \n",
    "    fea_是否行内对手 = fea_是否行内对手.merge(t_行内, how=\"left\", on=\"客户编号\")\n",
    "    fea_是否行内对手 = fea_是否行内对手.merge(t_行外, how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    fea_是否行内对手 = fea_是否行内对手.fillna(0)\n",
    "    fea_是否行内对手[\"对手方行内转账金额占比\"] = fea_是否行内对手[\"采样前120天客户_行内对手_折人民币交易总金额\"] / fea_是否行内对手[\"采样前120天客户折人民币交易总金额\"]\n",
    "    fea_是否行内对手[\"对手方行内_行外转账金额占比\"] = fea_是否行内对手[\"采样前120天客户_行内对手_折人民币交易总金额\"] / fea_是否行内对手[\"采样前120天客户_行外对手_折人民币交易总金额\"]\n",
    "    fea_是否行内对手 = fea_是否行内对手.drop(columns=[\"采样前120天客户折人民币交易总金额\"])\n",
    "\n",
    "    \n",
    "    fea_final = fea_同名转账占比.merge(fea_采样前一周交易笔数, how=\"left\", on=\"客户编号\")\n",
    "    fea_final = fea_final.merge(fea_不常见交易, how=\"left\", on=\"客户编号\")\n",
    "    fea_final = fea_final.merge(fea_分渠道交易, how=\"left\", on=\"客户编号\")\n",
    "    fea_final = fea_final.merge(fea_对公个人交易金额, how=\"left\", on=\"客户编号\")\n",
    "    fea_final = fea_final.merge(fea_转入转出交易金额, how=\"left\", on=\"客户编号\")\n",
    "    fea_final = fea_final.merge(fea_是否跨行交易金额, how=\"left\", on=\"客户编号\")\n",
    "    \n",
    "    fea_final = fea_final.merge(fea_是否行内对手, how=\"left\", on=\"客户编号\")\n",
    "\n",
    "    return fea_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3580fa46-1a8e-4db0-bb5e-729af0d1b455",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:53:28.241605Z",
     "iopub.status.busy": "2024-11-10T13:53:28.241169Z",
     "iopub.status.idle": "2024-11-10T13:55:52.207562Z",
     "msg_id": "91c00cf3-f4bc-4e81-8d76-4d96c95872fc",
     "shell.execute_reply": "2024-11-10T13:55:52.206732Z",
     "shell.execute_reply.started": "2024-11-10T13:53:28.241575Z"
    }
   },
   "outputs": [],
   "source": [
    "fea_final = 企业金融性交易明细_第二版简单逻辑特征()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "138ed9c2-fdc8-4d03-a428-c206582849e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:55:52.209349Z",
     "iopub.status.busy": "2024-11-10T13:55:52.208985Z",
     "iopub.status.idle": "2024-11-10T13:55:52.271350Z",
     "msg_id": "b8aea10d-7b8f-45ea-8d89-80c9605b2eab",
     "shell.execute_reply": "2024-11-10T13:55:52.270693Z",
     "shell.execute_reply.started": "2024-11-10T13:55:52.209321Z"
    }
   },
   "outputs": [],
   "source": [
    "fea_final.to_pickle(\"../data/数据还原_企业金融性交易流水_简单逻辑特征_1107.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "703dfc80-4f02-4589-91d6-1c939d7c13ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:56:42.104038Z",
     "iopub.status.busy": "2024-11-10T13:56:42.103544Z",
     "iopub.status.idle": "2024-11-10T13:56:42.127417Z",
     "msg_id": "e3a96378-8cce-4042-867f-3b6d5ce5b26c",
     "shell.execute_reply": "2024-11-10T13:56:42.126725Z",
     "shell.execute_reply.started": "2024-11-10T13:56:42.104006Z"
    }
   },
   "outputs": [],
   "source": [
    "o_df_2 = pd.read_pickle(\"/home/mole/work/xukunzhou/20241029/data/企业金融性交易流水_简单逻辑特征.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a7c522a-327c-4d55-813c-d1ac682c00eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:56:52.500089Z",
     "iopub.status.busy": "2024-11-10T13:56:52.499601Z",
     "iopub.status.idle": "2024-11-10T13:56:52.522919Z",
     "msg_id": "e3075a21-0aa8-455b-a2c9-29ddd4b38c9b",
     "shell.execute_reply": "2024-11-10T13:56:52.522221Z",
     "shell.execute_reply.started": "2024-11-10T13:56:52.500058Z"
    }
   },
   "outputs": [],
   "source": [
    "df_2 = pd.read_pickle(\"/home/mole/work/xukunzhou/复现/A榜/data/数据还原_企业金融性交易流水_简单逻辑特征_1107.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33685231-f71d-4155-b92d-52a812f125e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-10T13:57:03.109283Z",
     "iopub.status.busy": "2024-11-10T13:57:03.108772Z",
     "iopub.status.idle": "2024-11-10T13:57:03.137912Z",
     "msg_id": "5e39da31-8d0d-4be0-bbf6-2c30eb221f57",
     "shell.execute_reply": "2024-11-10T13:57:03.137246Z",
     "shell.execute_reply.started": "2024-11-10T13:57:03.109250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o_df_2.equals(df_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cd8bc0f-85e9-4e3e-8123-c5cd60f927cc",
   "metadata": {},
   "source": [
    "# 特征初步筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "501523af-3340-401b-8d3b-6417807f189b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "129a1ac5-ba1c-44a9-b6ac-a51df3efc452",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "d4012bcf-afb4-4892-a982-6fb22cc1308f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:38:11.152909Z",
     "iopub.status.busy": "2024-10-29T12:38:11.152377Z",
     "iopub.status.idle": "2024-10-29T12:38:11.186068Z",
     "msg_id": "36be450c-dbf4-4e3c-8190-e968534e2484",
     "shell.execute_reply": "2024-10-29T12:38:11.185357Z",
     "shell.execute_reply.started": "2024-10-29T12:38:11.152878Z"
    }
   },
   "outputs": [],
   "source": [
    "df_tyy = pd.read_pickle(\"/home/mole/work/tangyuyao/1029/data/企业金融性交易明细1029V4.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "01284c5c-bfd7-4620-88c6-a4eefeeb58d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:38:13.108413Z",
     "iopub.status.busy": "2024-10-29T12:38:13.107820Z",
     "iopub.status.idle": "2024-10-29T12:38:19.434304Z",
     "msg_id": "a28cec60-c1a4-4e0b-a36a-beedff3ecafc",
     "shell.execute_reply": "2024-10-29T12:38:19.433408Z",
     "shell.execute_reply.started": "2024-10-29T12:38:13.108382Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "df_xkz1 = pd.read_pickle(\"/home/mole/work/xukunzhou/20241028/data/企业金融性交易明细_差分特征.pkl\")\n",
    "keep_feas_xkz1 = [c for c in df_xkz1.columns.tolist() if \"30天\" in c]\n",
    "df_xkz1 = df_xkz1[[\"客户编号\"] + keep_feas_xkz]\n",
    "\n",
    "keep_feas_xkz2 = [\"客户编号\"] + ['合约账户余额_金融性交易_个人_按月流水_mean_min_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_对公_按月流水_mean_min_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_转出_按月流水_mean_mean_差分特征_金融性交易_转出_按月流水', '合约账户余额_金融性交易_个人_按月流水_mean_mean_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_对公_按月流水_mean_mean_差分特征_金融性交易_对公_按月流水', '一阶差分_合约账户余额_金融性交易_对公_按月流水_mean_-1_mean_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_少见交易_按月流水_mean_min_差分特征_金融性交易_少见交易_按月流水', '一阶差分_合约账户余额_金融性交易_对公_按月流水_count_-1_last_差分特征_金融性交易_对公_按月流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_mean_-1_std_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_转出_按月流水_mean_min_差分特征_金融性交易_转出_按月流水', '折人民币交易金额_金融性交易_对公_按月流水_sum_skew_差分特征_金融性交易_对公_按月流水', '一阶差分_合约账户余额_金融性交易_个人_按月流水_count_-1_min_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_对公_按月流水_mean_-1_max_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_对公_按月流水_count_skew_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_少见交易_按月流水_mean_mean_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_个人_按月流水_count_std_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_sum_mean_差分特征_金融性交易_少见交易_按月流水', '一阶差分_合约账户余额_金融性交易_转出_按月流水_mean_-1_max_差分特征_金融性交易_转出_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_sum_-1_std_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_mean_-1_last_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_last_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_常见交易_按月流水_sum_skew_差分特征_金融性交易_常见交易_按月流水', '一阶差分_合约账户余额_金融性交易_对公_按月流水_mean_-1_sum_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_对公_按月流水_sum_mean_差分特征_金融性交易_对公_按月流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_sum_-1_min_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_个人_按月流水_mean_mean_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_少见交易_按月流水_mean_sum_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_按月流水_mean_min_差分特征_金融性交易_按月流水', '一阶差分_合约账户余额_金融性交易_转出_按月流水_mean_-1_min_差分特征_金融性交易_转出_按月流水', '折人民币交易金额_金融性交易_个人_按月流水_sum_std_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_常见交易_按月流水_count_skew_差分特征_金融性交易_常见交易_按月流水', '合约账户余额_金融性交易_个人_按月流水_sum_skew_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_sum_差分特征_金融性交易_少见交易_按月流水', '折人民币交易金额_金融性交易_个人_按月流水_mean_skew_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_个人_按月流水_count_sum_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_常见交易_按月流水_count_-1_max_差分特征_金融性交易_常见交易_按月流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_mean_-1_last_差分特征_金融性交易_对公_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_sum_-1_min_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_个人_按月流水_mean_sum_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_个人_按月流水_count_skew_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_mean_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_个人_按月流水_count_mean_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_金融性交易_按月流水_sum_min_差分特征_金融性交易_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_std_差分特征_金融性交易_少见交易_按月流水', '一阶差分_合约账户余额_金融性交易_按月流水_mean_-1_sum_差分特征_金融性交易_按月流水', '折人民币交易金额_金融性交易_对公_按月流水_sum_min_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_max_差分特征_金融性交易_少见交易_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_sum_-1_max_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_转出_按月流水_mean_sum_差分特征_金融性交易_转出_按月流水', '折人民币交易金额_金融性交易_少见交易_按月流水_sum_last_差分特征_金融性交易_少见交易_按月流水', '一阶差分_合约账户余额_金融性交易_常见交易_按月流水_count_-1_last_差分特征_金融性交易_常见交易_按月流水', '折人民币交易金额_金融性交易_对公_按月流水_mean_mean_差分特征_金融性交易_对公_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_mean_-1_min_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_sum_-1_last_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_对公_按月流水_sum_sum_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_转出_按月流水_mean_last_差分特征_金融性交易_转出_按月流水', '折人民币交易金额_金融性交易_个人_按月流水_sum_max_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_转出_按月流水_mean_-1_last_差分特征_金融性交易_转出_按月流水', '合约账户余额_金融性交易_少见交易_按月流水_sum_min_差分特征_金融性交易_少见交易_按月流水', '一阶差分_合约账户余额_金融性交易_个人_按月流水_count_-1_std_差分特征_金融性交易_个人_按月流水']\n",
    "df_xkz2 = pd.read_pickle(\"./data/金融性交易明细_第二版差分特征.pkl\")[keep_feas_xkz2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "d3be011e-f68a-400f-9e9d-defeba0801bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:38:32.460231Z",
     "iopub.status.busy": "2024-10-29T12:38:32.459752Z",
     "iopub.status.idle": "2024-10-29T12:38:32.487119Z",
     "msg_id": "a2e51534-f435-450d-8a6c-e9f70488e07b",
     "shell.execute_reply": "2024-10-29T12:38:32.486341Z",
     "shell.execute_reply.started": "2024-10-29T12:38:32.460200Z"
    }
   },
   "outputs": [],
   "source": [
    "df_hyy = pd.read_pickle(\"/home/mole/work/heyuyang/1029/data/流水表文本特征_2030点.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "1a4d9a24-f1b9-40b2-bf0c-1bce7923a6ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:39:35.669718Z",
     "iopub.status.busy": "2024-10-29T12:39:35.669233Z",
     "iopub.status.idle": "2024-10-29T12:39:37.805881Z",
     "msg_id": "98c3e670-5b02-4056-8f72-53d902267e55",
     "shell.execute_reply": "2024-10-29T12:39:37.804975Z",
     "shell.execute_reply.started": "2024-10-29T12:39:35.669686Z"
    }
   },
   "outputs": [],
   "source": [
    "df = TARGET[[\"客户编号\",\"FLAG\"]].merge(df_tyy, how=\"left\", on=\"客户编号\")\n",
    "df = df.merge(df_xkz1, how=\"left\", on=\"客户编号\")\n",
    "df = df.merge(df_xkz2, how=\"left\", on=\"客户编号\")\n",
    "df = df.merge(df_hyy, how=\"left\", on=\"客户编号\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "3586906b-c498-4e6b-ac37-efd3178f3a05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:39:39.289019Z",
     "iopub.status.busy": "2024-10-29T12:39:39.288548Z",
     "iopub.status.idle": "2024-10-29T12:39:39.294293Z",
     "msg_id": "427523e2-f957-456a-8328-300d99930cf2",
     "shell.execute_reply": "2024-10-29T12:39:39.293585Z",
     "shell.execute_reply.started": "2024-10-29T12:39:39.288989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59079, 641)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cec7cf-dd51-4613-b9af-2a87d8f9f4bb",
   "metadata": {},
   "source": [
    "## 全特征训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "e5ee94a6-9d50-4951-8a1e-f8942d809567",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T11:17:44.846021Z",
     "iopub.status.busy": "2024-10-29T11:17:44.845395Z",
     "iopub.status.idle": "2024-10-29T11:17:44.856173Z",
     "msg_id": "b3c01170-5adf-478c-9cd5-d24afdffc784",
     "shell.execute_reply": "2024-10-29T11:17:44.855404Z",
     "shell.execute_reply.started": "2024-10-29T11:17:44.845989Z"
    }
   },
   "outputs": [],
   "source": [
    "# # N = 60 KS = 0.47231    # 线下KS最高\n",
    "feas_v1_best = ['合约账户余额_金融性交易_记账方向代码164_30天流水_mean_mean_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_现转标识1_30天流水_mean_min_差分特征_金融性交易_现转标识1_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_mean_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_min_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_min_差分特征_金融性交易_钞汇标识0_30天流水', '同名账户标识_count_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_月数_std_最近1个月_出帐流水信息', '合约账户余额_min_最近1个月_出帐流水信息', '同名账户标识_count_最近1个月_出帐流水信息', '合约账户余额_金融性交易_30天流水_mean_min_差分特征_金融性交易_30天流水', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_mean_差分特征_金融性交易_现转标识1_30天流水', '交易日期_距离_最近一笔交易_天数_min_最近1个月_出帐流水信息', '摘要信息_count_最近1个月_进帐流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_sum_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_min_最近1个月_出帐流水信息', '交易日期_距离_最近一笔交易_月数_std_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_max_最近1个月_常见交易_流水信息', '折人民币交易金额_min_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_mean_差分特征_金融性交易_钞汇标识0_30天流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_sum_skew_差分特征_金融性交易_记账方向代码125_30天流水', '折人民币交易金额_min_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_mean_差分特征_金融性交易_记账方向代码125_30天流水', '同名账户标识_unique_counts_最近1个月_出帐流水信息', '折人民币交易金额_mean_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_sum_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_std_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_std_最近1个月_常见交易_流水信息', '摘要信息_count_最近1个月_常见交易_流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_30天流水_mean_mean_差分特征_金融性交易_30天流水', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_min_差分特征_金融性交易_记账方向代码125_30天流水', '同名账户标识_count_最近1个月_外汇标识_金融资产信息', '交易日期_距离_最近一笔交易_天数_std_最近1个月_进帐流水信息', '合约账户余额_std_最近1个月_进帐流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_max_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_min_最近1个月_流水信息', '交易日期_距离_最近一笔交易_天数_max_最近1个月_出帐流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_mean_最近1个月_进帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_月数_mean_最近1个月_交易对手类型高违约率_流水信息', '摘要信息_count_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_skew_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_skew_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_skew_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_sum_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_现转标识1_30天流水_mean_mean_差分特征_金融性交易_现转标识1_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_sum_差分特征_金融性交易_记账方向代码125_30天流水', '是否跨行交易_unique_counts_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_进帐流水信息', '是否跨行交易_unique_counts_最近1个月_出帐流水信息']\n",
    "feas_v2_简单衍生 = ['采样前120天_同名转账笔数占比', '采样前7天内金融性交易笔数', '采样前30天内金融性交易笔数', '采样前120天内金融性交易笔数', '采样前7天内金融性交易笔数_占比', '采样前30天内金融性交易笔数_占比', '采样前120天少见类型交易笔数', '采样前120天内少见类型交易笔数_占比', '采样前120天少见渠道交易笔数', '采样前120天频率最高两类渠道交易笔数', '采样前120天内少见渠道交易笔数_占比', '采样前120天内频率最高两类渠道交易笔数_占比', '采样前120天客户_对公_折人民币交易总金额', '采样前120天客户_个人_折人民币交易总金额', '对公交易金额占比', '对公_个人交易金额占比', '采样前120天客户_转出_折人民币交易总金额', '采样前120天客户_转入_折人民币交易总金额', '转出金额占比', '转出_转入金额占比', '采样前120天客户_行内_折人民币交易总金额', '采样前120天客户_跨行_折人民币交易总金额', '行内转账金额占比', '行内_行外转账金额占比', '采样前120天客户_行内对手_折人民币交易总金额', '采样前120天客户_行外对手_折人民币交易总金额', '对手方行内转账金额占比', '对手方行内_行外转账金额占比']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "088c1e75-1b4e-43e2-a323-a88ce769532f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:39:55.928760Z",
     "iopub.status.busy": "2024-10-29T12:39:55.928275Z",
     "iopub.status.idle": "2024-10-29T12:39:57.251063Z",
     "msg_id": "02843bfd-4707-4cfd-9f08-4ed544aecaf8",
     "shell.execute_reply": "2024-10-29T12:39:57.250164Z",
     "shell.execute_reply.started": "2024-10-29T12:39:55.928721Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df[df[\"FLAG\"].notnull()].drop(columns=[\"客户编号\", \"FLAG\"])\n",
    "y = df[df[\"FLAG\"].notnull()][\"FLAG\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "1f00b683-d950-4db6-9f5f-1ba0023c96aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:39:59.883258Z",
     "iopub.status.busy": "2024-10-29T12:39:59.882783Z",
     "iopub.status.idle": "2024-10-29T12:43:48.630181Z",
     "msg_id": "3104b398-3d7b-4013-9090-1a0b8d792351",
     "shell.execute_reply": "2024-10-29T12:43:48.629415Z",
     "shell.execute_reply.started": "2024-10-29T12:39:59.883228Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.778964\tValid's auc: 0.766338\n",
      "[200]\tTrain's auc: 0.793379\tValid's auc: 0.777649\n",
      "[300]\tTrain's auc: 0.807092\tValid's auc: 0.786196\n",
      "[400]\tTrain's auc: 0.817266\tValid's auc: 0.791528\n",
      "[500]\tTrain's auc: 0.828091\tValid's auc: 0.798388\n",
      "[600]\tTrain's auc: 0.837313\tValid's auc: 0.803693\n",
      "[700]\tTrain's auc: 0.845054\tValid's auc: 0.807493\n",
      "[800]\tTrain's auc: 0.852224\tValid's auc: 0.810622\n",
      "[900]\tTrain's auc: 0.858753\tValid's auc: 0.81279\n",
      "[1000]\tTrain's auc: 0.864705\tValid's auc: 0.814281\n",
      "[1100]\tTrain's auc: 0.869458\tValid's auc: 0.815832\n",
      "[1200]\tTrain's auc: 0.87404\tValid's auc: 0.817366\n",
      "[1300]\tTrain's auc: 0.87857\tValid's auc: 0.818299\n",
      "[1400]\tTrain's auc: 0.882594\tValid's auc: 0.820076\n",
      "[1500]\tTrain's auc: 0.886985\tValid's auc: 0.821417\n",
      "[1600]\tTrain's auc: 0.891218\tValid's auc: 0.822444\n",
      "[1700]\tTrain's auc: 0.895\tValid's auc: 0.823133\n",
      "[1800]\tTrain's auc: 0.898701\tValid's auc: 0.82324\n",
      "[1900]\tTrain's auc: 0.901785\tValid's auc: 0.823614\n",
      "[2000]\tTrain's auc: 0.905093\tValid's auc: 0.824238\n",
      "[2100]\tTrain's auc: 0.908189\tValid's auc: 0.824982\n",
      "[2200]\tTrain's auc: 0.911182\tValid's auc: 0.8253\n",
      "[2300]\tTrain's auc: 0.913861\tValid's auc: 0.825419\n",
      "[2400]\tTrain's auc: 0.916865\tValid's auc: 0.82608\n",
      "[2500]\tTrain's auc: 0.919271\tValid's auc: 0.826394\n",
      "[2600]\tTrain's auc: 0.921768\tValid's auc: 0.826428\n",
      "[2700]\tTrain's auc: 0.924161\tValid's auc: 0.826647\n",
      "[2800]\tTrain's auc: 0.926481\tValid's auc: 0.827411\n",
      "[2900]\tTrain's auc: 0.928635\tValid's auc: 0.827544\n",
      "[3000]\tTrain's auc: 0.930395\tValid's auc: 0.827302\n",
      "[3100]\tTrain's auc: 0.932601\tValid's auc: 0.827328\n",
      "Early stopping, best iteration is:\n",
      "[2931]\tTrain's auc: 0.92914\tValid's auc: 0.827663\n",
      "=======================================\n",
      "第 1 折，当前 KS = 0.52273\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.780843\tValid's auc: 0.736912\n",
      "[200]\tTrain's auc: 0.796036\tValid's auc: 0.750931\n",
      "[300]\tTrain's auc: 0.8076\tValid's auc: 0.7625\n",
      "[400]\tTrain's auc: 0.819006\tValid's auc: 0.772976\n",
      "[500]\tTrain's auc: 0.830126\tValid's auc: 0.780506\n",
      "[600]\tTrain's auc: 0.839983\tValid's auc: 0.786952\n",
      "[700]\tTrain's auc: 0.848344\tValid's auc: 0.790858\n",
      "[800]\tTrain's auc: 0.855056\tValid's auc: 0.793575\n",
      "[900]\tTrain's auc: 0.861349\tValid's auc: 0.795794\n",
      "[1000]\tTrain's auc: 0.866642\tValid's auc: 0.797104\n",
      "[1100]\tTrain's auc: 0.871846\tValid's auc: 0.798319\n",
      "[1200]\tTrain's auc: 0.876726\tValid's auc: 0.799889\n",
      "[1300]\tTrain's auc: 0.880962\tValid's auc: 0.800858\n",
      "[1400]\tTrain's auc: 0.885546\tValid's auc: 0.801821\n",
      "[1500]\tTrain's auc: 0.889149\tValid's auc: 0.802518\n",
      "[1600]\tTrain's auc: 0.892768\tValid's auc: 0.803203\n",
      "[1700]\tTrain's auc: 0.896392\tValid's auc: 0.803219\n",
      "[1800]\tTrain's auc: 0.899324\tValid's auc: 0.803248\n",
      "[1900]\tTrain's auc: 0.902745\tValid's auc: 0.803772\n",
      "[2000]\tTrain's auc: 0.906366\tValid's auc: 0.804325\n",
      "[2100]\tTrain's auc: 0.909362\tValid's auc: 0.805001\n",
      "[2200]\tTrain's auc: 0.912206\tValid's auc: 0.805182\n",
      "[2300]\tTrain's auc: 0.915121\tValid's auc: 0.805085\n",
      "Early stopping, best iteration is:\n",
      "[2197]\tTrain's auc: 0.912141\tValid's auc: 0.805213\n",
      "=======================================\n",
      "第 2 折，当前 KS = 0.4639\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.778801\tValid's auc: 0.78278\n",
      "[200]\tTrain's auc: 0.792442\tValid's auc: 0.790615\n",
      "[300]\tTrain's auc: 0.804924\tValid's auc: 0.796852\n",
      "[400]\tTrain's auc: 0.816793\tValid's auc: 0.80431\n",
      "[500]\tTrain's auc: 0.827486\tValid's auc: 0.808484\n",
      "[600]\tTrain's auc: 0.837393\tValid's auc: 0.81324\n",
      "[700]\tTrain's auc: 0.846071\tValid's auc: 0.816182\n",
      "[800]\tTrain's auc: 0.853084\tValid's auc: 0.817541\n",
      "[900]\tTrain's auc: 0.859575\tValid's auc: 0.818793\n",
      "[1000]\tTrain's auc: 0.865422\tValid's auc: 0.819919\n",
      "[1100]\tTrain's auc: 0.870385\tValid's auc: 0.820642\n",
      "[1200]\tTrain's auc: 0.875377\tValid's auc: 0.821534\n",
      "[1300]\tTrain's auc: 0.880461\tValid's auc: 0.822227\n",
      "[1400]\tTrain's auc: 0.884736\tValid's auc: 0.823298\n",
      "[1500]\tTrain's auc: 0.888913\tValid's auc: 0.823468\n",
      "[1600]\tTrain's auc: 0.89278\tValid's auc: 0.823389\n",
      "[1700]\tTrain's auc: 0.896774\tValid's auc: 0.824022\n",
      "[1800]\tTrain's auc: 0.900376\tValid's auc: 0.82407\n",
      "[1900]\tTrain's auc: 0.903949\tValid's auc: 0.824453\n",
      "[2000]\tTrain's auc: 0.907146\tValid's auc: 0.8249\n",
      "[2100]\tTrain's auc: 0.910044\tValid's auc: 0.824815\n",
      "[2200]\tTrain's auc: 0.913031\tValid's auc: 0.82462\n",
      "Early stopping, best iteration is:\n",
      "[2011]\tTrain's auc: 0.907553\tValid's auc: 0.824936\n",
      "=======================================\n",
      "第 3 折，当前 KS = 0.500692\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.778172\tValid's auc: 0.767302\n",
      "[200]\tTrain's auc: 0.79216\tValid's auc: 0.776868\n",
      "[300]\tTrain's auc: 0.803714\tValid's auc: 0.787519\n",
      "[400]\tTrain's auc: 0.81539\tValid's auc: 0.797654\n",
      "[500]\tTrain's auc: 0.825295\tValid's auc: 0.804843\n",
      "[600]\tTrain's auc: 0.835563\tValid's auc: 0.810906\n",
      "[700]\tTrain's auc: 0.843829\tValid's auc: 0.81545\n",
      "[800]\tTrain's auc: 0.851366\tValid's auc: 0.819588\n",
      "[900]\tTrain's auc: 0.857617\tValid's auc: 0.821734\n",
      "[1000]\tTrain's auc: 0.863376\tValid's auc: 0.823715\n",
      "[1100]\tTrain's auc: 0.86894\tValid's auc: 0.825708\n",
      "[1200]\tTrain's auc: 0.873982\tValid's auc: 0.827096\n",
      "[1300]\tTrain's auc: 0.877821\tValid's auc: 0.828076\n",
      "[1400]\tTrain's auc: 0.882351\tValid's auc: 0.828595\n",
      "[1500]\tTrain's auc: 0.886739\tValid's auc: 0.82943\n",
      "[1600]\tTrain's auc: 0.890744\tValid's auc: 0.829531\n",
      "[1700]\tTrain's auc: 0.894553\tValid's auc: 0.829993\n",
      "[1800]\tTrain's auc: 0.898217\tValid's auc: 0.830215\n",
      "[1900]\tTrain's auc: 0.901747\tValid's auc: 0.831069\n",
      "[2000]\tTrain's auc: 0.905285\tValid's auc: 0.83193\n",
      "[2100]\tTrain's auc: 0.908424\tValid's auc: 0.8318\n",
      "Early stopping, best iteration is:\n",
      "[1993]\tTrain's auc: 0.905008\tValid's auc: 0.832006\n",
      "=======================================\n",
      "第 4 折，当前 KS = 0.527189\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.782081\tValid's auc: 0.750922\n",
      "[200]\tTrain's auc: 0.794613\tValid's auc: 0.760537\n",
      "[300]\tTrain's auc: 0.808537\tValid's auc: 0.77484\n",
      "[400]\tTrain's auc: 0.820031\tValid's auc: 0.782818\n",
      "[500]\tTrain's auc: 0.830986\tValid's auc: 0.787962\n",
      "[600]\tTrain's auc: 0.841278\tValid's auc: 0.792397\n",
      "[700]\tTrain's auc: 0.849399\tValid's auc: 0.796364\n",
      "[800]\tTrain's auc: 0.856331\tValid's auc: 0.799282\n",
      "[900]\tTrain's auc: 0.862972\tValid's auc: 0.801477\n",
      "[1000]\tTrain's auc: 0.868698\tValid's auc: 0.802799\n",
      "[1100]\tTrain's auc: 0.873823\tValid's auc: 0.803705\n",
      "[1200]\tTrain's auc: 0.878298\tValid's auc: 0.805147\n",
      "[1300]\tTrain's auc: 0.882756\tValid's auc: 0.805621\n",
      "[1400]\tTrain's auc: 0.886571\tValid's auc: 0.806328\n",
      "[1500]\tTrain's auc: 0.890777\tValid's auc: 0.80726\n",
      "[1600]\tTrain's auc: 0.894406\tValid's auc: 0.807958\n",
      "[1700]\tTrain's auc: 0.898142\tValid's auc: 0.808125\n",
      "[1800]\tTrain's auc: 0.901473\tValid's auc: 0.808502\n",
      "[1900]\tTrain's auc: 0.904975\tValid's auc: 0.80891\n",
      "[2000]\tTrain's auc: 0.908082\tValid's auc: 0.808889\n",
      "[2100]\tTrain's auc: 0.91124\tValid's auc: 0.809792\n",
      "[2200]\tTrain's auc: 0.914353\tValid's auc: 0.80957\n",
      "[2300]\tTrain's auc: 0.917136\tValid's auc: 0.810213\n",
      "[2400]\tTrain's auc: 0.91979\tValid's auc: 0.810102\n",
      "[2500]\tTrain's auc: 0.922281\tValid's auc: 0.81072\n",
      "[2600]\tTrain's auc: 0.924482\tValid's auc: 0.810903\n",
      "[2700]\tTrain's auc: 0.926986\tValid's auc: 0.811173\n",
      "[2800]\tTrain's auc: 0.92939\tValid's auc: 0.810529\n",
      "[2900]\tTrain's auc: 0.931986\tValid's auc: 0.810978\n",
      "Early stopping, best iteration is:\n",
      "[2700]\tTrain's auc: 0.926986\tValid's auc: 0.811173\n",
      "=======================================\n",
      "第 5 折，当前 KS = 0.476461\n",
      "=======================================\n",
      "平均 KS = 0.498194\n",
      "Out of folds KS = 0.492652\n",
      "平均 AUC = 0.820198\n",
      "Out of folds AUC = 0.819919\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "     'boost_from_average':'false',\n",
    "     'boost':'gbdt',\n",
    "     'objective': 'binary',\n",
    "     'num_threads': 8,\n",
    "     'metrics': 'auc',\n",
    "     'learning_rate':0.01,\n",
    "     'first_metric_only': True,\n",
    "     'verbose': -1,\n",
    "     'bagging_fraction': 0.8350064977659397,\n",
    "     'bagging_freq': 5,\n",
    "     'feature_fraction': 0.9956069033278404,\n",
    "     'min_child_samples': 4,\n",
    "     'max_depth':3,\n",
    "     'num_leaves': 12,\n",
    "     'reg_alpha': 1,\n",
    "     'reg_lambda': 4\n",
    "}\n",
    "clfs = LGB_model(X, y, params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "db5ce62a-9ea9-48bb-b3c5-6b94e721f391",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:43:59.354224Z",
     "iopub.status.busy": "2024-10-29T12:43:59.353694Z",
     "iopub.status.idle": "2024-10-29T12:44:00.148624Z",
     "msg_id": "e88cc87a-76a9-42c0-90f5-15162a029696",
     "shell.execute_reply": "2024-10-29T12:44:00.147906Z",
     "shell.execute_reply.started": "2024-10-29T12:43:59.354193Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fea = get_feature_imp(clfs[0], feature_names=X.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ea034c7-9854-4d26-ac9b-e519cbda6a09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "b55babd5-8a90-4722-b37f-cfccc62e89a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:56:48.391386Z",
     "iopub.status.busy": "2024-10-29T12:56:48.390598Z",
     "iopub.status.idle": "2024-10-29T12:56:48.400324Z",
     "msg_id": "c6d2bbb6-165d-4e53-9ae5-59ae847d8a51",
     "shell.execute_reply": "2024-10-29T12:56:48.399630Z",
     "shell.execute_reply.started": "2024-10-29T12:56:48.391355Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['合约账户余额_金融性交易_个人_按月流水_mean_min_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_对公_按月流水_mean_min_差分特征_金融性交易_对公_按月流水', '最终剩余余额', '交易代码_tfidf_8', '合约账户余额_金融性交易_个人_按月流水_mean_mean_差分特征_金融性交易_个人_按月流水', '摘要信息_tfidf_8', '合约账户余额_金融性交易_转出_按月流水_mean_mean_差分特征_金融性交易_转出_按月流水', '合约账户余额_金融性交易_对公_按月流水_mean_mean_差分特征_金融性交易_对公_按月流水', '客户编号_摘要信息_w2v_2', '客户编号_渠道代码_w2v_6', '合约账户余额_金融性交易_少见交易_按月流水_mean_min_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_mean_差分特征_金融性交易_记账方向代码125_30天流水', '渠道代码_tfidf_4', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_mean_差分特征_金融性交易_记账方向代码164_30天流水', '渠道代码_tfidf_6', '渠道代码_tfidf_9', '客户编号_交易代码_w2v_7', '合约账户余额_min_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_对公_按月流水_count_-1_last_差分特征_金融性交易_对公_按月流水', '摘要信息_tfidf_4', '摘要信息_tfidf_9', '交易代码_tfidf_3', '客户编号_交易代码_w2v_2', '一阶差分_合约账户余额_金融性交易_对公_按月流水_mean_-1_max_差分特征_金融性交易_对公_按月流水', '客户编号_渠道代码_w2v_7', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_mean_-1_last_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_对公_按月流水_mean_-1_mean_差分特征_金融性交易_对公_按月流水', '客户编号_摘要信息_w2v_7', '摘要信息_countvec_3', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_min_差分特征_金融性交易_记账方向代码164_30天流水', '客户编号_交易代码_w2v_3', '交易日期_距离_最近一笔交易_天数_min_最近1个月_出帐流水信息', '折人民币交易金额_min_最近1个月_出帐流水信息', '交易日期_距离_最近一笔交易_天数_max_最近1个月_出帐流水信息', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_sum_skew_差分特征_金融性交易_记账方向代码125_30天流水', '摘要信息_countvec_4', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_std_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_常见交易_按月流水_sum_skew_差分特征_金融性交易_常见交易_按月流水', '客户编号_交易代码_w2v_1', '合约账户余额_max_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_mean_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_min_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_月数_std_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_std_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_月数_std_最近1个月_出帐流水信息', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_sum_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_转出_按月流水_mean_min_差分特征_金融性交易_转出_按月流水', '合约账户余额_金融性交易_少见交易_按月流水_sum_min_差分特征_金融性交易_少见交易_按月流水', '摘要信息_countvec_9', '同名账户标识_count_最近1个月_常见交易_流水信息', '客户编号_交易对手客户编号_w2v_7', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_sum_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_min_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_金融性交易_个人_按月流水_sum_skew_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_个人_按月流水_count_-1_min_差分特征_金融性交易_个人_按月流水', '交易代码_countvec_1', '客户编号_摘要信息_w2v_0', '合约账户余额_金融性交易_个人_按月流水_count_skew_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_sum_-1_min_差分特征_金融性交易_对公_按月流水', '渠道代码_tfidf_2', '折人民币交易金额_金融性交易_按月流水_sum_min_差分特征_金融性交易_按月流水', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_常见交易_流水信息', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_last_差分特征_金融性交易_少见交易_按月流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '交易代码_tfidf_4', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_skew_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_mean_差分特征_金融性交易_少见交易_按月流水', '渠道代码_countvec_4', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_min_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_mean_最近1个月_进帐流水信息', '折人民币交易金额_金融性交易_对公_按月流水_sum_skew_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_个人_按月流水_mean_mean_差分特征_金融性交易_个人_按月流水', '客户编号_渠道代码_w2v_4', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_sum_-1_min_差分特征_金融性交易_个人_按月流水', '客户编号_交易代码_w2v_5', '客户编号_摘要信息_w2v_5', '折人民币交易金额_金融性交易_个人_按月流水_mean_skew_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_skew_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_max_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_常见交易_按月流水_count_skew_差分特征_金融性交易_常见交易_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_sum_-1_max_差分特征_金融性交易_个人_按月流水', '折人民币交易金额_mean_最近1个月_常见交易_流水信息', '渠道代码_tfidf_8', '折人民币交易金额_min_最近1个月_流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_mean_最近1个月_出帐流水信息', '折人民币交易金额_金融性交易_少见交易_按月流水_mean_max_差分特征_金融性交易_少见交易_按月流水', '折人民币交易金额_std_最近1个月_常见交易_流水信息', '渠道代码_tfidf_7', '摘要信息_tfidf_7', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_mean_-1_min_差分特征_金融性交易_个人_按月流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_skew_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_个人_按月流水_count_sum_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_转出_按月流水_mean_-1_min_差分特征_金融性交易_转出_按月流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_mean_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_少见交易_按月流水_mean_sum_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_个人_按月流水_mean_sum_差分特征_金融性交易_个人_按月流水', '摘要信息_countvec_5', '客户编号_交易代码_w2v_4', '交易代码_countvec_8', '摘要信息_tfidf_6', '合约账户余额_std_最近1个月_进帐流水信息', '交易日期_距离_最近一笔交易_天数_std_最近1个月_进帐流水信息', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_skew_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_mean_-1_std_差分特征_金融性交易_对公_按月流水', '一阶差分_折人民币交易金额_金融性交易_个人_按月流水_sum_-1_std_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_sum_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_mean_差分特征_金融性交易_钞汇标识0_30天流水', '渠道代码_tfidf_3', '一阶差分_合约账户余额_金融性交易_常见交易_按月流水_count_-1_last_差分特征_金融性交易_常见交易_按月流水', '交易日期_距离_最近一笔交易_天数_min_最近1个月_流水信息', '一阶差分_合约账户余额_金融性交易_对公_按月流水_mean_-1_sum_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_少见交易_按月流水_mean_mean_差分特征_金融性交易_少见交易_按月流水', '一阶差分_折人民币交易金额_金融性交易_转出_按月流水_mean_-1_last_差分特征_金融性交易_转出_按月流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_sum_skew_差分特征_金融性交易_记账方向代码164_30天流水', '交易日期_距离_最近一笔交易_月数_mean_最近1个月_交易对手类型高违约率_流水信息', '渠道代码_tfidf_0', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_sum_-1_last_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_转出_按月流水_mean_sum_差分特征_金融性交易_转出_按月流水', '交易代码_tfidf_2', '折人民币交易金额_std_最近1个月_交易对手类型高违约率_流水信息', '折人民币交易金额_min_最近1个月_进帐流水信息', '折人民币交易金额_金融性交易_个人_按月流水_sum_max_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_std_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_sum_skew_差分特征_金融性交易_记账方向代码125_30天流水', '折人民币交易金额_max_最近1个月_常见交易_流水信息', '交易代码_tfidf_5', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_last_差分特征_金融性交易_记账方向代码164_30天流水', '客户编号_渠道代码_w2v_1', '客户编号_交易对手客户编号_w2v_5', '折人民币交易金额_金融性交易_对公_按月流水_sum_min_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_30天流水_mean_mean_差分特征_金融性交易_30天流水', '渠道代码_countvec_6', '客户编号_摘要信息_w2v_3', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_sum_-1_mean_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_sum_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '摘要信息_count_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_min_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_sum_std_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_合约账户余额_金融性交易_常见交易_按月流水_count_-1_max_差分特征_金融性交易_常见交易_按月流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_sum_差分特征_金融性交易_记账方向代码164_30天流水', '交易日期_距离_最近一笔交易_月数_std_最近1个月_进帐流水信息', '合约账户余额_金融性交易_个人_按月流水_count_std_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_sum_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_少见交易_按月流水_sum_last_差分特征_金融性交易_少见交易_按月流水', '合约账户余额_金融性交易_对公_按月流水_count_skew_差分特征_金融性交易_对公_按月流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_sum_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码125_30天流水', '摘要信息_countvec_1', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_mean_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_对公_按月流水_mean_mean_差分特征_金融性交易_对公_按月流水', '折人民币交易金额_金融性交易_记账方向代码164_30天流水_mean_skew_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_sum_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_min_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_月数_std_最近1个月_常见交易_流水信息', '折人民币交易金额_金融性交易_现转标识1_30天流水_sum_skew_差分特征_金融性交易_现转标识1_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码125_30天流水', '交易对手客户编号_tfidf_0', '渠道代码_countvec_8', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_出帐流水信息', '合约账户余额_std_最近1个月_出帐流水信息', '客户编号_交易对手客户编号_w2v_0', '合约账户余额_金融性交易_记账方向代码125_30天流水_sum_min_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_金融性交易_个人_按月流水_count_mean_差分特征_金融性交易_个人_按月流水', '一阶差分_合约账户余额_金融性交易_转出_按月流水_mean_-1_max_差分特征_金融性交易_转出_按月流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_last_差分特征_金融性交易_记账方向代码164_30天流水', '摘要信息_countvec_2', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_std_差分特征_金融性交易_记账方向代码164_30天流水', '客户编号_摘要信息_w2v_1', '交易代码_tfidf_9', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_mean_差分特征_金融性交易_记账方向代码125_30天流水', '渠道代码_countvec_7', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_std_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_折人民币交易金额_金融性交易_对公_按月流水_mean_-1_last_差分特征_金融性交易_对公_按月流水', '交易日期_距离_最近一笔交易_月数_mean_最近1个月_出帐流水信息', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_sum_min_差分特征_金融性交易_记账方向代码125_30天流水', '合约账户余额_max_最近1个月_进帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_sum_-1_sum_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_天数_std_最近1个月_常见交易_流水信息', '折人民币交易金额_金融性交易_对公_按月流水_sum_mean_差分特征_金融性交易_对公_按月流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_钞汇标识0_30天流水_mean_-1_sum_差分特征_金融性交易_钞汇标识0_30天流水', '渠道代码_countvec_2', '一阶差分_合约账户余额_金融性交易_个人_按月流水_count_-1_std_差分特征_金融性交易_个人_按月流水', '合约账户余额_mean_最近1个月_常见交易_流水信息', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_skew_差分特征_金融性交易_钞汇标识0_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_sum_-1_std_差分特征_金融性交易_记账方向代码125_30天流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_mean_-1_sum_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_last_差分特征_金融性交易_记账方向代码125_30天流水', '折人民币交易金额_max_最近1个月_出帐流水信息', '交易代码_countvec_6', '折人民币交易金额_金融性交易_少见交易_按月流水_sum_mean_差分特征_金融性交易_少见交易_按月流水', '交易日期_距离_最近一笔交易_月数_mean_最近1个月_进帐流水信息', '折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_std_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_个人_按月流水_sum_std_差分特征_金融性交易_个人_按月流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_mean_-1_mean_差分特征_金融性交易_记账方向代码164_30天流水']\n"
     ]
    }
   ],
   "source": [
    "# N = 100 KS = 0.467682\n",
    "# N = 80 KS = 0.466028\n",
    "# N = 120 KS = 0.468984\n",
    "# N = 150 KS = 0.463996\n",
    "# N = 50 KS = 0.467686\n",
    "# N = 35 KS = 0.464533\n",
    "# N = 60 KS = 0.47231    # 线下KS最高\n",
    "# N = 65 KS = 0.470072\n",
    "# N = 55 KS = 0.469775\n",
    "\n",
    "# N = 60 KS = 0.478119\n",
    "# N = 70 KS = 0.475046\n",
    "# N = 50 KS = 0.473987\n",
    "# N = 65 KS = 0.478664 feas = ['合约账户余额_金融性交易_记账方向代码164_30天流水_mean_mean_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_现转标识1_30天流水_mean_min_差分特征_金融性交易_现转标识1_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_mean_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_月数_std_最近1个月_出帐流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_min_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_min_差分特征_金融性交易_钞汇标识0_30天流水', '合约账户余额_min_最近1个月_出帐流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '同名账户标识_count_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_天数_min_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_mean_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_std_最近1个月_交易对手类型高违约率_流水信息', '摘要信息_count_最近1个月_进帐流水信息', '折人民币交易金额_min_最近1个月_常见交易_流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '交易日期_距离_最近一笔交易_月数_mean_最近1个月_交易对手类型高违约率_流水信息', '采样前120天_同名转账笔数占比', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_sum_skew_差分特征_金融性交易_记账方向代码125_30天流水', '折人民币交易金额_min_最近1个月_出帐流水信息', '合约账户余额_max_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_月数_std_最近1个月_交易对手类型高违约率_流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_sum_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_mean_最近1个月_常见交易_流水信息', '合约账户余额_金融性交易_30天流水_mean_min_差分特征_金融性交易_30天流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_sum_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_std_最近1个月_常见交易_流水信息', '折人民币交易金额_min_最近1个月_交易对手类型高违约率_流水信息', '同名账户标识_unique_counts_最近1个月_出帐流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_skew_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_std_最近1个月_进帐流水信息', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_mean_差分特征_金融性交易_钞汇标识0_30天流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_mean_差分特征_金融性交易_记账方向代码125_30天流水', '采样前120天内频率最高两类渠道交易笔数_占比', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_min_差分特征_金融性交易_记账方向代码125_30天流水', '采样前30天内金融性交易笔数_占比', '折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_skew_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_skew_差分特征_金融性交易_记账方向代码125_30天流水', '同名账户标识_count_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_天数_std_最近1个月_进帐流水信息', '采样前120天客户_跨行_折人民币交易总金额', '折人民币交易金额_min_最近1个月_流水信息', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_sum_差分特征_金融性交易_记账方向代码125_30天流水', '采样前120天客户_转入_折人民币交易总金额', '合约账户余额_mean_最近1个月_进帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '行内_行外转账金额占比', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_进帐流水信息', '交易日期_距离_最近一笔交易_天数_max_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_sum_差分特征_金融性交易_记账方向代码125_30天流水', '采样前120天客户_行内_折人民币交易总金额', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_max_差分特征_金融性交易_记账方向代码164_30天流水', '摘要信息_count_最近1个月_常见交易_流水信息', '转出金额占比', '摘要信息_count_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_金融性交易_30天流水_mean_mean_差分特征_金融性交易_30天流水', '是否跨行交易_unique_counts_最近1个月_常见交易_流水信息', '是否跨行交易_unique_counts_最近1个月_出帐流水信息', '采样前120天客户_个人_折人民币交易总金额', '采样前120天内金融性交易笔数', '采样前120天客户_转出_折人民币交易总金额', '采样前7天内金融性交易笔数_占比']\n",
    "\n",
    "\n",
    "# 新差分特征\n",
    "# N = 50 KS = 0.443885\n",
    "# N = 40 KS = 0.435798\n",
    "# N = 60 KS = 0.447594\n",
    "# N = 70 KS = 0.442627\n",
    "# N = 60\n",
    "\n",
    "# N = 120 KS = 0.490798\n",
    "# N = 80 KS = 0.489129\n",
    "# N = 200 KS = 0.492594\n",
    "# N = 300 KS = 0.490846\n",
    "# N = 250 KS = 0.491454\n",
    "N = 200\n",
    "fea_top_N = fea.head(N)[\"feature\"].values.tolist()\n",
    "print(fea_top_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "1654e876-3c7e-4375-ab75-83619d23761d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:33:28.793536Z",
     "iopub.status.busy": "2024-10-29T12:33:28.792841Z",
     "iopub.status.idle": "2024-10-29T12:33:28.804570Z",
     "msg_id": "0fc03c41-fa3b-4121-8092-0a078286e019",
     "shell.execute_reply": "2024-10-29T12:33:28.803899Z",
     "shell.execute_reply.started": "2024-10-29T12:33:28.793505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feas = ['合约账户余额_金融性交易_记账方向代码164_30天流水_mean_mean_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_现转标识1_30天流水_mean_min_差分特征_金融性交易_现转标识1_30天流水', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_mean_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_月数_std_最近1个月_出帐流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_min_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_min_差分特征_金融性交易_钞汇标识0_30天流水', '合约账户余额_min_最近1个月_出帐流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '同名账户标识_count_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_天数_min_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_mean_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_std_最近1个月_交易对手类型高违约率_流水信息', '摘要信息_count_最近1个月_进帐流水信息', '折人民币交易金额_min_最近1个月_常见交易_流水信息', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '交易日期_距离_最近一笔交易_月数_mean_最近1个月_交易对手类型高违约率_流水信息', '采样前120天_同名转账笔数占比', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_sum_skew_差分特征_金融性交易_记账方向代码125_30天流水', '折人民币交易金额_min_最近1个月_出帐流水信息', '合约账户余额_max_最近1个月_常见交易_流水信息', '交易日期_距离_最近一笔交易_月数_std_最近1个月_交易对手类型高违约率_流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_常见交易_流水信息', '一阶差分_合约账户余额_金融性交易_现转标识1_30天流水_mean_-1_sum_差分特征_金融性交易_现转标识1_30天流水', '折人民币交易金额_mean_最近1个月_常见交易_流水信息', '合约账户余额_金融性交易_30天流水_mean_min_差分特征_金融性交易_30天流水', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_sum_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_std_最近1个月_常见交易_流水信息', '折人民币交易金额_min_最近1个月_交易对手类型高违约率_流水信息', '同名账户标识_unique_counts_最近1个月_出帐流水信息', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_skew_差分特征_金融性交易_记账方向代码164_30天流水', '合约账户余额_std_最近1个月_进帐流水信息', '合约账户余额_金融性交易_钞汇标识0_30天流水_mean_mean_差分特征_金融性交易_钞汇标识0_30天流水', '一阶差分_折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_-1_max_差分特征_金融性交易_记账方向代码164_30天流水', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_mean_差分特征_金融性交易_记账方向代码125_30天流水', '采样前120天内频率最高两类渠道交易笔数_占比', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_min_差分特征_金融性交易_记账方向代码125_30天流水', '采样前30天内金融性交易笔数_占比', '折人民币交易金额_金融性交易_记账方向代码164_30天流水_sum_skew_差分特征_金融性交易_记账方向代码164_30天流水', '折人民币交易金额_金融性交易_记账方向代码125_30天流水_mean_skew_差分特征_金融性交易_记账方向代码125_30天流水', '同名账户标识_count_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_last_差分特征_金融性交易_记账方向代码125_30天流水', '交易日期_距离_最近一笔交易_天数_std_最近1个月_进帐流水信息', '采样前120天客户_跨行_折人民币交易总金额', '折人民币交易金额_min_最近1个月_流水信息', '合约账户余额_金融性交易_记账方向代码125_30天流水_mean_sum_差分特征_金融性交易_记账方向代码125_30天流水', '采样前120天客户_转入_折人民币交易总金额', '合约账户余额_mean_最近1个月_进帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码164_30天流水_mean_-1_std_差分特征_金融性交易_记账方向代码164_30天流水', '行内_行外转账金额占比', '交易日期_距离_最近一笔交易_天数_mean_最近1个月_进帐流水信息', '交易日期_距离_最近一笔交易_天数_max_最近1个月_出帐流水信息', '一阶差分_合约账户余额_金融性交易_记账方向代码125_30天流水_mean_-1_sum_差分特征_金融性交易_记账方向代码125_30天流水', '采样前120天客户_行内_折人民币交易总金额', '合约账户余额_金融性交易_记账方向代码164_30天流水_mean_max_差分特征_金融性交易_记账方向代码164_30天流水', '摘要信息_count_最近1个月_常见交易_流水信息', '转出金额占比', '摘要信息_count_最近1个月_交易对手类型高违约率_流水信息', '合约账户余额_金融性交易_30天流水_mean_mean_差分特征_金融性交易_30天流水', '是否跨行交易_unique_counts_最近1个月_常见交易_流水信息', '是否跨行交易_unique_counts_最近1个月_出帐流水信息', '采样前120天客户_个人_折人民币交易总金额', '采样前120天内金融性交易笔数', '采样前120天客户_转出_折人民币交易总金额', '采样前7天内金融性交易笔数_占比']\n",
    "len(feas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1143e42a-30be-48a4-b620-38ff43a8290f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "49bb0f1d-6cea-448e-bbed-baae48a634ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T12:54:07.921452Z",
     "iopub.status.busy": "2024-10-29T12:54:07.920989Z",
     "iopub.status.idle": "2024-10-29T12:56:14.881319Z",
     "msg_id": "37585899-e8ea-4bf2-8948-6ae66ece6651",
     "shell.execute_reply": "2024-10-29T12:56:14.880565Z",
     "shell.execute_reply.started": "2024-10-29T12:54:07.921420Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.779266\tValid's auc: 0.76605\n",
      "[200]\tTrain's auc: 0.792934\tValid's auc: 0.777053\n",
      "[300]\tTrain's auc: 0.806949\tValid's auc: 0.786715\n",
      "[400]\tTrain's auc: 0.816992\tValid's auc: 0.791848\n",
      "[500]\tTrain's auc: 0.827655\tValid's auc: 0.798763\n",
      "[600]\tTrain's auc: 0.836893\tValid's auc: 0.804032\n",
      "[700]\tTrain's auc: 0.84459\tValid's auc: 0.807974\n",
      "[800]\tTrain's auc: 0.851879\tValid's auc: 0.81109\n",
      "[900]\tTrain's auc: 0.858052\tValid's auc: 0.814283\n",
      "[1000]\tTrain's auc: 0.864332\tValid's auc: 0.815531\n",
      "[1100]\tTrain's auc: 0.868837\tValid's auc: 0.816919\n",
      "[1200]\tTrain's auc: 0.873466\tValid's auc: 0.818391\n",
      "[1300]\tTrain's auc: 0.877989\tValid's auc: 0.8196\n",
      "[1400]\tTrain's auc: 0.882011\tValid's auc: 0.821529\n",
      "[1500]\tTrain's auc: 0.886138\tValid's auc: 0.822723\n",
      "[1600]\tTrain's auc: 0.890133\tValid's auc: 0.823475\n",
      "[1700]\tTrain's auc: 0.893806\tValid's auc: 0.824282\n",
      "[1800]\tTrain's auc: 0.897518\tValid's auc: 0.824802\n",
      "[1900]\tTrain's auc: 0.900565\tValid's auc: 0.825224\n",
      "[2000]\tTrain's auc: 0.903718\tValid's auc: 0.82557\n",
      "[2100]\tTrain's auc: 0.906922\tValid's auc: 0.826276\n",
      "[2200]\tTrain's auc: 0.910002\tValid's auc: 0.82679\n",
      "[2300]\tTrain's auc: 0.912635\tValid's auc: 0.827052\n",
      "[2400]\tTrain's auc: 0.915371\tValid's auc: 0.827953\n",
      "[2500]\tTrain's auc: 0.91768\tValid's auc: 0.828554\n",
      "[2600]\tTrain's auc: 0.920311\tValid's auc: 0.828437\n",
      "[2700]\tTrain's auc: 0.922714\tValid's auc: 0.828262\n",
      "[2800]\tTrain's auc: 0.925064\tValid's auc: 0.828934\n",
      "[2900]\tTrain's auc: 0.927595\tValid's auc: 0.828654\n",
      "Early stopping, best iteration is:\n",
      "[2795]\tTrain's auc: 0.924963\tValid's auc: 0.828943\n",
      "=======================================\n",
      "第 1 折，当前 KS = 0.52608\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.780774\tValid's auc: 0.738442\n",
      "[200]\tTrain's auc: 0.794745\tValid's auc: 0.751162\n",
      "[300]\tTrain's auc: 0.807268\tValid's auc: 0.763342\n",
      "[400]\tTrain's auc: 0.818577\tValid's auc: 0.773252\n",
      "[500]\tTrain's auc: 0.829374\tValid's auc: 0.780817\n",
      "[600]\tTrain's auc: 0.839067\tValid's auc: 0.787545\n",
      "[700]\tTrain's auc: 0.847277\tValid's auc: 0.791678\n",
      "[800]\tTrain's auc: 0.853855\tValid's auc: 0.794147\n",
      "[900]\tTrain's auc: 0.860094\tValid's auc: 0.796149\n",
      "[1000]\tTrain's auc: 0.865354\tValid's auc: 0.797909\n",
      "[1100]\tTrain's auc: 0.870599\tValid's auc: 0.799121\n",
      "[1200]\tTrain's auc: 0.875525\tValid's auc: 0.800767\n",
      "[1300]\tTrain's auc: 0.879806\tValid's auc: 0.801248\n",
      "[1400]\tTrain's auc: 0.884372\tValid's auc: 0.8024\n",
      "[1500]\tTrain's auc: 0.887897\tValid's auc: 0.802885\n",
      "[1600]\tTrain's auc: 0.891477\tValid's auc: 0.803516\n",
      "[1700]\tTrain's auc: 0.894973\tValid's auc: 0.803521\n",
      "[1800]\tTrain's auc: 0.897936\tValid's auc: 0.804118\n",
      "[1900]\tTrain's auc: 0.901131\tValid's auc: 0.80379\n",
      "[2000]\tTrain's auc: 0.904533\tValid's auc: 0.803962\n",
      "Early stopping, best iteration is:\n",
      "[1814]\tTrain's auc: 0.898446\tValid's auc: 0.804279\n",
      "=======================================\n",
      "第 2 折，当前 KS = 0.462834\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.777812\tValid's auc: 0.781634\n",
      "[200]\tTrain's auc: 0.792575\tValid's auc: 0.79092\n",
      "[300]\tTrain's auc: 0.804834\tValid's auc: 0.797052\n",
      "[400]\tTrain's auc: 0.816547\tValid's auc: 0.804627\n",
      "[500]\tTrain's auc: 0.827096\tValid's auc: 0.808892\n",
      "[600]\tTrain's auc: 0.836714\tValid's auc: 0.813243\n",
      "[700]\tTrain's auc: 0.845191\tValid's auc: 0.816283\n",
      "[800]\tTrain's auc: 0.852167\tValid's auc: 0.817546\n",
      "[900]\tTrain's auc: 0.85889\tValid's auc: 0.819043\n",
      "[1000]\tTrain's auc: 0.8646\tValid's auc: 0.820519\n",
      "[1100]\tTrain's auc: 0.869388\tValid's auc: 0.821442\n",
      "[1200]\tTrain's auc: 0.874418\tValid's auc: 0.822352\n",
      "[1300]\tTrain's auc: 0.879621\tValid's auc: 0.823377\n",
      "[1400]\tTrain's auc: 0.88379\tValid's auc: 0.824497\n",
      "[1500]\tTrain's auc: 0.887877\tValid's auc: 0.825127\n",
      "[1600]\tTrain's auc: 0.891526\tValid's auc: 0.824992\n",
      "[1700]\tTrain's auc: 0.895333\tValid's auc: 0.825399\n",
      "[1800]\tTrain's auc: 0.898883\tValid's auc: 0.82509\n",
      "[1900]\tTrain's auc: 0.902328\tValid's auc: 0.825769\n",
      "[2000]\tTrain's auc: 0.905554\tValid's auc: 0.825768\n",
      "[2100]\tTrain's auc: 0.908279\tValid's auc: 0.825929\n",
      "[2200]\tTrain's auc: 0.9114\tValid's auc: 0.825911\n",
      "[2300]\tTrain's auc: 0.914387\tValid's auc: 0.826204\n",
      "[2400]\tTrain's auc: 0.91676\tValid's auc: 0.825882\n",
      "[2500]\tTrain's auc: 0.919322\tValid's auc: 0.825432\n",
      "Early stopping, best iteration is:\n",
      "[2355]\tTrain's auc: 0.915569\tValid's auc: 0.826243\n",
      "=======================================\n",
      "第 3 折，当前 KS = 0.504571\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.778884\tValid's auc: 0.769432\n",
      "[200]\tTrain's auc: 0.791942\tValid's auc: 0.778699\n",
      "[300]\tTrain's auc: 0.803694\tValid's auc: 0.789527\n",
      "[400]\tTrain's auc: 0.815065\tValid's auc: 0.798967\n",
      "[500]\tTrain's auc: 0.825114\tValid's auc: 0.805927\n",
      "[600]\tTrain's auc: 0.835242\tValid's auc: 0.81165\n",
      "[700]\tTrain's auc: 0.84344\tValid's auc: 0.816512\n",
      "[800]\tTrain's auc: 0.850974\tValid's auc: 0.820406\n",
      "[900]\tTrain's auc: 0.856824\tValid's auc: 0.822345\n",
      "[1000]\tTrain's auc: 0.862704\tValid's auc: 0.82429\n",
      "[1100]\tTrain's auc: 0.86805\tValid's auc: 0.826286\n",
      "[1200]\tTrain's auc: 0.872959\tValid's auc: 0.82784\n",
      "[1300]\tTrain's auc: 0.876925\tValid's auc: 0.828866\n",
      "[1400]\tTrain's auc: 0.881331\tValid's auc: 0.829296\n",
      "[1500]\tTrain's auc: 0.885349\tValid's auc: 0.830034\n",
      "[1600]\tTrain's auc: 0.889279\tValid's auc: 0.830257\n",
      "[1700]\tTrain's auc: 0.892831\tValid's auc: 0.831073\n",
      "[1800]\tTrain's auc: 0.896355\tValid's auc: 0.830969\n",
      "[1900]\tTrain's auc: 0.899898\tValid's auc: 0.831493\n",
      "[2000]\tTrain's auc: 0.90323\tValid's auc: 0.832328\n",
      "[2100]\tTrain's auc: 0.906393\tValid's auc: 0.832201\n",
      "[2200]\tTrain's auc: 0.909537\tValid's auc: 0.832092\n",
      "[2300]\tTrain's auc: 0.912541\tValid's auc: 0.832669\n",
      "[2400]\tTrain's auc: 0.915163\tValid's auc: 0.832691\n",
      "[2500]\tTrain's auc: 0.917987\tValid's auc: 0.833134\n",
      "[2600]\tTrain's auc: 0.920325\tValid's auc: 0.833361\n",
      "[2700]\tTrain's auc: 0.922692\tValid's auc: 0.833176\n",
      "Early stopping, best iteration is:\n",
      "[2520]\tTrain's auc: 0.918322\tValid's auc: 0.833452\n",
      "=======================================\n",
      "第 4 折，当前 KS = 0.522771\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.781571\tValid's auc: 0.754205\n",
      "[200]\tTrain's auc: 0.794309\tValid's auc: 0.762177\n",
      "[300]\tTrain's auc: 0.808073\tValid's auc: 0.775316\n",
      "[400]\tTrain's auc: 0.819709\tValid's auc: 0.783713\n",
      "[500]\tTrain's auc: 0.830491\tValid's auc: 0.788605\n",
      "[600]\tTrain's auc: 0.840371\tValid's auc: 0.793127\n",
      "[700]\tTrain's auc: 0.848792\tValid's auc: 0.797156\n",
      "[800]\tTrain's auc: 0.855506\tValid's auc: 0.800387\n",
      "[900]\tTrain's auc: 0.861982\tValid's auc: 0.802491\n",
      "[1000]\tTrain's auc: 0.867524\tValid's auc: 0.803768\n",
      "[1100]\tTrain's auc: 0.872497\tValid's auc: 0.804534\n",
      "[1200]\tTrain's auc: 0.876671\tValid's auc: 0.806374\n",
      "[1300]\tTrain's auc: 0.880917\tValid's auc: 0.807788\n",
      "[1400]\tTrain's auc: 0.884841\tValid's auc: 0.808385\n",
      "[1500]\tTrain's auc: 0.889112\tValid's auc: 0.809091\n",
      "[1600]\tTrain's auc: 0.892599\tValid's auc: 0.80973\n",
      "[1700]\tTrain's auc: 0.896167\tValid's auc: 0.810015\n",
      "[1800]\tTrain's auc: 0.899539\tValid's auc: 0.810361\n",
      "[1900]\tTrain's auc: 0.902847\tValid's auc: 0.810715\n",
      "[2000]\tTrain's auc: 0.905995\tValid's auc: 0.810836\n",
      "[2100]\tTrain's auc: 0.909196\tValid's auc: 0.811347\n",
      "[2200]\tTrain's auc: 0.912254\tValid's auc: 0.811135\n",
      "[2300]\tTrain's auc: 0.914861\tValid's auc: 0.811326\n",
      "Early stopping, best iteration is:\n",
      "[2125]\tTrain's auc: 0.910043\tValid's auc: 0.81159\n",
      "=======================================\n",
      "第 5 折，当前 KS = 0.472557\n",
      "=======================================\n",
      "平均 KS = 0.497763\n",
      "Out of folds KS = 0.491454\n",
      "平均 AUC = 0.820902\n",
      "Out of folds AUC = 0.820873\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "     'boost_from_average':'false',\n",
    "     'boost':'gbdt',\n",
    "     'objective': 'binary',\n",
    "     'num_threads': 8,\n",
    "     'metrics': 'auc',\n",
    "     'learning_rate':0.01,\n",
    "     'first_metric_only': True,\n",
    "     'verbose': -1,\n",
    "     'bagging_fraction': 0.8350064977659397,\n",
    "     'bagging_freq': 5,\n",
    "     'feature_fraction': 0.9956069033278404,\n",
    "     'min_child_samples': 4,\n",
    "     'max_depth':3,\n",
    "     'num_leaves': 12,\n",
    "     'reg_alpha': 1,\n",
    "     'reg_lambda': 4\n",
    "}\n",
    "clfs = LGB_model(X[fea_top_N], y, params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3afb2535-4c4e-4f52-baa4-c132497842ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f14cac-7848-46c9-9d1c-c2de30caf2b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "598997c0-f699-42ed-a82d-c5dd737c3247",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "d271ff59-7652-4e03-b200-cc13bb08c4ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-28T09:58:55.083271Z",
     "iopub.status.busy": "2024-10-28T09:58:55.082758Z",
     "iopub.status.idle": "2024-10-28T09:58:55.856871Z",
     "msg_id": "da2725bf-c627-4652-b7d7-c6428d88eb59",
     "shell.execute_reply": "2024-10-28T09:58:55.856155Z",
     "shell.execute_reply.started": "2024-10-28T09:58:55.083238Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fea = get_feature_imp(clfs[0], feature_names=X[selected_fea].columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d5fc0938-edc7-45b5-b6cb-f11809f92d4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-28T10:00:59.577978Z",
     "iopub.status.busy": "2024-10-28T10:00:59.577473Z",
     "iopub.status.idle": "2024-10-28T10:00:59.583007Z",
     "msg_id": "e7194388-ba77-42d1-9e06-ea7fe7c52f68",
     "shell.execute_reply": "2024-10-28T10:00:59.582306Z",
     "shell.execute_reply.started": "2024-10-28T10:00:59.577945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['一阶差分_应纳税额_10109_纳税流水_sum_-1_mean_差分特征_10109_纳税流水', '减免税额_max_最近6个月申报_税种10101', '减免税额_10101_纳税流水_mean_min_差分特征_10101_纳税流水', '减免税额_max_最近24个月申报_税种10101', '全部销售收入_mean_最近24个月申报_税种10104', '全部销售收入_sum_最近24个月申报_税种10101', '全部销售收入_10101_纳税流水_mean_sum_差分特征_10101_纳税流水', '企业税率_min_最近12个月申报_所有税种', '应纳税额_sum_最近12个月申报_税种0', '一阶差分_应纳税额_10109_纳税流水_sum_-1_sum_差分特征_10109_纳税流水', '企业税率_min_最近6个月申报_所有税种', '一阶差分_减免税额_30216_纳税流水_mean_-1_sum_差分特征_30216_纳税流水', '应税销售收入_全部纳税流水_mean_last_差分特征_全部纳税流水', '减免税额_std_最近24个月申报_税种10109', '全部销售收入_10104_纳税流水_mean_sum_差分特征_10104_纳税流水', '企业税率_max_最近24个月申报_税种10101', '一阶差分_应税销售收入_全部纳税流水_mean_-1_sum_差分特征_全部纳税流水', '一阶差分_应税销售收入_10109_纳税流水_sum_-1_sum_差分特征_10109_纳税流水', '应税销售收入_10101_纳税流水_mean_std_差分特征_10101_纳税流水', '应纳税额_std_最近24个月申报_税种10101', '应纳税额_10101_纳税流水_sum_min_差分特征_10101_纳税流水', '一阶差分_全部销售收入_10101_纳税流水_mean_-1_sum_差分特征_10101_纳税流水', '减免税额_10101_纳税流水_sum_max_差分特征_10101_纳税流水', '全部销售收入_全部纳税流水_mean_sum_差分特征_全部纳税流水', '是否在申报期限内申报_mean_最近24个月申报_所有税种', '企业税率_mean_最近12个月申报_所有税种', '应纳税额_mean_最近6个月申报_税种10101', '一阶差分_应税销售收入_全部纳税流水_sum_-1_sum_差分特征_全部纳税流水', '一阶差分_全部销售收入_10104_纳税流水_mean_-1_min_差分特征_10104_纳税流水', '企业税率_mean_最近24个月申报_税种10101']\n"
     ]
    }
   ],
   "source": [
    "N = 30 # KS = 0.21\n",
    "fea_top_N = fea.head(N)[\"feature\"].values.tolist()\n",
    "print(fea_top_N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "dd21f328-630f-4d08-b605-11bf73f04200",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-28T10:01:00.545501Z",
     "iopub.status.busy": "2024-10-28T10:01:00.545084Z",
     "iopub.status.idle": "2024-10-28T10:01:10.738467Z",
     "msg_id": "b95e09f1-8684-4c7c-a1dd-43e28529af9b",
     "shell.execute_reply": "2024-10-28T10:01:10.737714Z",
     "shell.execute_reply.started": "2024-10-28T10:01:00.545471Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.629298\tValid's auc: 0.602013\n",
      "[200]\tTrain's auc: 0.688723\tValid's auc: 0.597561\n",
      "[300]\tTrain's auc: 0.714361\tValid's auc: 0.601262\n",
      "[400]\tTrain's auc: 0.729471\tValid's auc: 0.599419\n",
      "[500]\tTrain's auc: 0.7451\tValid's auc: 0.603961\n",
      "[600]\tTrain's auc: 0.759611\tValid's auc: 0.607261\n",
      "[700]\tTrain's auc: 0.771602\tValid's auc: 0.607961\n",
      "[800]\tTrain's auc: 0.785635\tValid's auc: 0.611936\n",
      "[900]\tTrain's auc: 0.798236\tValid's auc: 0.613621\n",
      "[1000]\tTrain's auc: 0.807341\tValid's auc: 0.616044\n",
      "[1100]\tTrain's auc: 0.817243\tValid's auc: 0.615208\n",
      "[1200]\tTrain's auc: 0.826735\tValid's auc: 0.614554\n",
      "Early stopping, best iteration is:\n",
      "[1055]\tTrain's auc: 0.81239\tValid's auc: 0.617597\n",
      "=======================================\n",
      "第 1 折，当前 KS = 0.179621\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.629708\tValid's auc: 0.610701\n",
      "[200]\tTrain's auc: 0.670985\tValid's auc: 0.636075\n",
      "[300]\tTrain's auc: 0.703061\tValid's auc: 0.658057\n",
      "[400]\tTrain's auc: 0.72368\tValid's auc: 0.665023\n",
      "[500]\tTrain's auc: 0.738713\tValid's auc: 0.675864\n",
      "[600]\tTrain's auc: 0.754686\tValid's auc: 0.676195\n",
      "[700]\tTrain's auc: 0.769936\tValid's auc: 0.678751\n",
      "[800]\tTrain's auc: 0.783844\tValid's auc: 0.680021\n",
      "[900]\tTrain's auc: 0.795462\tValid's auc: 0.678265\n",
      "Early stopping, best iteration is:\n",
      "[751]\tTrain's auc: 0.777235\tValid's auc: 0.68127\n",
      "=======================================\n",
      "第 2 折，当前 KS = 0.286795\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.617309\tValid's auc: 0.613912\n",
      "[200]\tTrain's auc: 0.673013\tValid's auc: 0.63578\n",
      "[300]\tTrain's auc: 0.701124\tValid's auc: 0.637125\n",
      "[400]\tTrain's auc: 0.720275\tValid's auc: 0.646345\n",
      "[500]\tTrain's auc: 0.738077\tValid's auc: 0.645037\n",
      "[600]\tTrain's auc: 0.755\tValid's auc: 0.644253\n",
      "Early stopping, best iteration is:\n",
      "[470]\tTrain's auc: 0.734087\tValid's auc: 0.646601\n",
      "=======================================\n",
      "第 3 折，当前 KS = 0.235617\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.5824\tValid's auc: 0.532541\n",
      "[200]\tTrain's auc: 0.677519\tValid's auc: 0.629978\n",
      "[300]\tTrain's auc: 0.715855\tValid's auc: 0.636618\n",
      "[400]\tTrain's auc: 0.734419\tValid's auc: 0.636626\n",
      "[500]\tTrain's auc: 0.749662\tValid's auc: 0.639066\n",
      "[600]\tTrain's auc: 0.765025\tValid's auc: 0.637684\n",
      "Early stopping, best iteration is:\n",
      "[435]\tTrain's auc: 0.73965\tValid's auc: 0.639803\n",
      "=======================================\n",
      "第 4 折，当前 KS = 0.232508\n",
      "=======================================\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[100]\tTrain's auc: 0.626546\tValid's auc: 0.589387\n",
      "[200]\tTrain's auc: 0.675989\tValid's auc: 0.611507\n",
      "[300]\tTrain's auc: 0.709847\tValid's auc: 0.621577\n",
      "[400]\tTrain's auc: 0.730912\tValid's auc: 0.627969\n",
      "[500]\tTrain's auc: 0.748561\tValid's auc: 0.631202\n",
      "[600]\tTrain's auc: 0.765363\tValid's auc: 0.625158\n",
      "[700]\tTrain's auc: 0.778988\tValid's auc: 0.62253\n",
      "Early stopping, best iteration is:\n",
      "[500]\tTrain's auc: 0.748561\tValid's auc: 0.631202\n",
      "=======================================\n",
      "第 5 折，当前 KS = 0.220372\n",
      "=======================================\n",
      "平均 KS = 0.230983\n",
      "Out of folds KS = 0.210827\n",
      "平均 AUC = 0.643295\n",
      "Out of folds AUC = 0.640907\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "     'boost_from_average':'false',\n",
    "     'boost':'gbdt',\n",
    "     'objective': 'binary',\n",
    "     'num_threads': 8,\n",
    "     'metrics': 'auc',\n",
    "     'learning_rate':0.01,\n",
    "     'first_metric_only': True,\n",
    "     'verbose': -1,\n",
    "     'bagging_fraction': 0.8350064977659397,\n",
    "     'bagging_freq': 5,\n",
    "     'feature_fraction': 0.9956069033278404,\n",
    "     'min_child_samples': 4,\n",
    "     'max_depth':3,\n",
    "     'num_leaves': 12,\n",
    "     'reg_alpha': 1,\n",
    "     'reg_lambda': 4\n",
    "}\n",
    "clfs = LGB_model(X[fea_top_N], y, params=params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abc38b4-5594-4d47-ae5c-ff6bc6d3d2e1",
   "metadata": {},
   "source": [
    "# 按季度/按年度差分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0851fa6-d549-4dbd-a3f4-ab94096595f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = copy.deepcopy(data)\n",
    "for is_train in [0, 1]:\n",
    "    data_agg = pd.DataFrame()\n",
    "        \n",
    "    temp1_diff = trend_indicator(df=df[(df[\"is_train\"]==is_train)],\n",
    "                                 group_dim_1=\"客户编号\",\n",
    "                                 group_dim_2=\"申报日期_距离_采样日期_季度数\",\n",
    "                                 agg_functions=agg_functions_diff, \n",
    "                                 name_flag=\"全部纳税流水_季度差分\")\n",
    "    \n",
    "    data_agg = copy.deepcopy(temp1_diff)\n",
    "\n",
    "    \n",
    "    del temp1_diff\n",
    "    gc.collect()\n",
    "\n",
    "    for cod in [10101, 0, 10109, 30203, 30216, 10104, 10111]:\n",
    "        # 0\n",
    "        temp2_diff = trend_indicator(df=df[(df[\"is_train\"]==is_train) & (df[\"征收项目代码\"]==cod)],\n",
    "                                     group_dim_1=\"客户编号\",\n",
    "                                     group_dim_2=\"申报日期_距离_采样日期_季度数\",\n",
    "                                     agg_functions=agg_functions_diff, \n",
    "                                     name_flag=\"{}_纳税流水_季度差分\".format(cod))\n",
    "        data_agg = data_agg.merge(temp2_diff, how=\"left\", on=\"客户编号\")\n",
    "\n",
    "        del temp2_diff\n",
    "        gc.collect()\n",
    "\n",
    "    temp1_diff = trend_indicator(df=df[(df[\"is_train\"]==is_train) ],\n",
    "                                 group_dim_1=\"客户编号\",\n",
    "                                 group_dim_2=\"申报日期_距离_采样日期_年份数\",\n",
    "                                 agg_functions=agg_functions_diff, \n",
    "                                 name_flag=\"全部纳税流水_年度差分\")\n",
    "    \n",
    "    data_agg = copy.deepcopy(temp1_diff)\n",
    "\n",
    "    \n",
    "    del temp1_diff\n",
    "    gc.collect()\n",
    "\n",
    "    for cod in [10101, 0, 10109, 30203, 30216, 10104, 10111]:\n",
    "        # 0\n",
    "        temp2_diff = trend_indicator(df=df[(df[\"is_train\"]==is_train)& (df[\"征收项目代码\"]==cod)],\n",
    "                                     group_dim_1=\"客户编号\",\n",
    "                                     group_dim_2=\"申报日期_距离_采样日期_年份数\",\n",
    "                                     agg_functions=agg_functions_diff, \n",
    "                                     name_flag=\"{}_纳税流水_年度差分\".format(cod))\n",
    "        data_agg = data_agg.merge(temp2_diff, how=\"left\", on=\"客户编号\")\n",
    "\n",
    "        del temp2_diff\n",
    "        gc.collect()\n",
    "        \n",
    "    if is_train == 0:\n",
    "        test_agg = copy.deepcopy(data_agg)\n",
    "    else:\n",
    "        train_agg = copy.deepcopy(data_agg)\n",
    "\n",
    "df_final_2 = train_agg_2.append(test_agg_2)"
   ]
  }
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